The night is thick with static inside your tenant, and the questions aren’t small anymore. Copilot can walk the clean, well-lit M365 streets — summarizing inbox noise, tightening your notes, finding what you already have permission to see. Fast, friendly, useful. But tone isn’t truth, and guesses don’t survive compliance.

This episode pulls you into the alleys where real knowledge lives: stale PDFs, forgotten SharePoint stacks, file-server ghosts, wikis no one maintained. That’s where Copilot reaches its boundary — and where Retrieval Augmented Generation starts. RAG becomes the librarian with receipts, dragging ground truth from your own systems, forcing citations, refusing to bluff. We map when Copilot is enough, when you must build a pipeline, and why teams explode cost, tickets, and trust by confusing the two. A secret step makes the whole discipline 10× easier — and we go there.

If your world runs on proprietary policy, SOPs, baselines, and high-stakes questions where wrong means risk, this is your compass. Copilot handles the errands. RAG handles the law. Pick the lane. Move.

In today's fast-paced world, selecting the right AI tool is crucial. About 78% of business leaders utilize AI in some capacity, indicating a growing demand for effective solutions. You may find yourself deciding between two powerful options: RAG vs Copilot.

RAG is designed to provide you with accurate information based on your company’s content, ensuring that you have reliable data at your fingertips. Conversely, Microsoft 365 Copilot enhances your productivity within the Microsoft 365 ecosystem by simplifying tasks. Understanding these distinctions can empower you to make informed choices that align with your business needs.

Key Takeaways

RAG gives correct and trustworthy information. It combines what you know with outside data. This makes it great for following rules and making decisions.
Microsoft 365 Copilot helps you work better. It makes tasks easier in Microsoft 365 apps. This lets users focus on important work, not just routine tasks.
Choosing RAG or Copilot depends on what your business needs. RAG is great for accuracy and rules, while Copilot is best for daily work and managing projects.
You can use both tools together. This way, you get RAG's accurate data and Copilot's speed. This improves how you work and make decisions.
Knowing what each tool is good at and where they fall short helps you make smart choices that fit your business goals.

Understanding RAG

Definition of RAG

Retrieval-Augmented Generation (RAG) is a strong method in artificial intelligence. It makes generative AI models more accurate and reliable. RAG does this by using information from important outside data sources. This method links large language models with different external resources. This helps them give trustworthy answers. Instead of just using internal knowledge, RAG makes sure the information you get is solid and dependable.

Strengths of RAG

RAG has many benefits that make it useful for businesses. Here are some main strengths:

Dynamic and Up-to-date Information: RAG systems mix old knowledge with current data. This makes sure answers show recent changes. This is important for smart decision-making in fast-moving fields like finance and healthcare.
Scalability: RAG architecture can handle big datasets well. It keeps working well even with a lot of information. This is important for businesses that need quick and accurate information retrieval.
Efficient Management of Data: RAG is great at pulling and combining important information from large data stores. This ability ensures quick outputs without slowing down, making it perfect for real-time data processing tasks.
Improved AI Reliability: By basing answers on trusted sources, RAG builds trust with users. This reliability is key for important decision-making, where accuracy is a must.

Microsoft 365 Copilot Overview

Features of Copilot

Microsoft 365 Copilot is a strong helper that boosts your work in many apps. Here are some key features that make it different from other AI tools:

Application	Core Features
Word	Write, change, and summarize documents using company files.
Excel	Find trends, create formulas, and show insights from data.
PowerPoint	Make presentations from a Word document or prompt, suggest slide designs.
Outlook	Write and summarize emails, suggest replies, organize your inbox.
Teams	Summarize missed meetings, create action items, recap chats.
SharePoint	Show relevant documents, create page summaries, help with content.
Power Automate	Build automation flows from simple descriptions, fix workflows.

These features help you finish tasks faster and work better, making your day easier.

Limitations of Copilot

Even though Microsoft 365 Copilot has many benefits, it also has some downsides. Knowing these can help you set realistic expectations. Here are some limitations based on what users say:

Use Case	Limitations
Summarizing Lengthy Documents	Might miss important details, so you need to check and add missing info.
Generating Meeting Notes	Gives general summaries but may skip important points, needing manual additions.
Summarizing, Drafting, and Refining Emails	Automated drafts might lack a personal touch and details, often needing edits.
Drafting Documents and Content	First drafts usually need a lot of changes for clarity and requirements.
Analyzing Data and Reporting	Limited for complex data tasks, often better to do manually.
Drafting Initial Presentations	You must adjust AI-made content to fit your style and brand.
Translating into Multiple Languages	Accuracy and context can be issues, needing checks by native speakers.
Comparing Documents	Has trouble with legal language details, needing human review for accuracy.
Searching for Documents, Emails, etc.	May struggle with context or specific keywords, leading to irrelevant results.

By knowing these limits, you can use Microsoft 365 Copilot better for your needs.

RAG vs Copilot: A Comparative Analysis

Key Differences

When you look at RAG and Microsoft 365 Copilot, you will see some important differences. These differences can help you choose the right tool for your needs. The way each tool is built affects how they work for your business. Here’s a simple comparison of their main features:

Feature	RAG Architecture	Microsoft 365 Copilot Architecture
Core Positioning	Focused on company knowledge and clear Q&A.	Built into Microsoft 365 tools for productivity.
Knowledge Base Integration	Changes company documents into a smart knowledge base.	Uses data from Microsoft Graph, like SharePoint and OneDrive.
Deployment Options	Can be set up on-site for better data control and rules.	Works within Microsoft’s cloud services.
Customization Capabilities	Highly customizable for managing knowledge and user experience.	Limited customization mainly at the app level.

These differences show that RAG focuses on data accuracy and control. Meanwhile, Copilot aims to boost productivity in the Microsoft environment.

Use Cases for Each Tool

Knowing when to use RAG or Copilot can help you pick the best tool for your business. Here are some common ways to use each one:

RAG Use Cases:

Compliance and Governance: RAG is great where data accuracy and rules matter. It gives reliable answers based on trusted sources, making it perfect for finance and healthcare.
Research and Development: Use RAG to find exact information from large databases. This helps your team make decisions based on the best data.
Legal and Regulatory: RAG helps legal teams by providing accurate references and citations, which are key for following rules and preparing cases.

Microsoft 365 Copilot Use Cases:

Daily Operations: Copilot makes everyday tasks easier, like writing emails, summarizing meetings, and creating reports. This saves time and boosts productivity.
Project Management: Use Copilot for planning and scheduling. It helps manage resources, timelines, and team efforts well.
Data Analysis: Copilot makes data insights easier by creating charts and summaries without complex formulas. This lets you focus on important decisions instead of manual data work.

By knowing these use cases, you can better match your choice of RAG or Copilot with your business goals.

Real-World Applications of RAG and Copilot

Industries Using RAG

Retrieval-Augmented Generation (RAG) is used in many different industries. Here are some important areas that use RAG solutions:

Finance: RAG helps banks and financial companies stay accurate and follow rules in their reports.
Law: Legal teams use RAG to find exact case laws and rules, making their research better.
Healthcare: RAG helps healthcare workers by giving them correct patient details and treatment advice.
Retail: Stores use RAG to look at customer data and manage their stock better.
Technology: Tech companies use RAG for developing products and improving customer support.

Companies using RAG see great results. For example, 86% of businesses add RAG methods to their generative AI. In healthcare, around 75% of people have RAG pilot projects going on. The global RAG market is expected to grow a lot, from USD 1.2 billion in 2023 to USD 11.0 billion by 2030, with a yearly growth rate of 49.1%. RAG systems also cut down AI mistakes by 70–90% compared to regular models, which helps users trust AI outputs more.

Industries Using Microsoft 365 Copilot

Microsoft 365 Copilot boosts productivity in many sectors. Here are some industries that gain from its features:

Education: Teachers use Copilot to make lesson plans and simplify admin tasks.
Marketing: Marketing teams use Copilot to write content and check how campaigns are doing.
Finance: Financial analysts use Copilot to automate reports and analyze data.
Healthcare: Healthcare workers benefit from Copilot's ability to summarize patient records and organize schedules.
Human Resources: HR teams use Copilot to write job descriptions and make onboarding easier.

The effects of Microsoft 365 Copilot are impressive. It automates boring tasks, letting teams focus on important work. For instance, Copilot cuts down manual work by quickly drafting documents and summarizing meeting notes. This efficiency leads to overall productivity gains across functions, adding up to $18.8 million in total benefits.

Bar chart showing percentage improvements in productivity metrics from Microsoft 365 Copilot

By knowing how RAG and Microsoft 365 Copilot work in real life, you can better decide which tool fits your business needs.

Choosing Between RAG and Copilot

Factors to Consider

When you pick between RAG and Microsoft 365 Copilot, think about some important factors. These will help you find the right tool for your business:

Cost: Look at the monthly costs for each option. RAG might need a lot of money upfront for setup and ongoing care. On the other hand, Microsoft 365 Copilot has different subscription plans that fit various budgets. Here’s a quick cost comparison:

Plan Type	Monthly Cost	Features
Free Tier	Free	Moderate response times, capped message volumes, limited image generation capabilities.
Plus Plan	$20/user/month	Good for small teams, includes access to GPT-4 and shared infrastructure.
Pro Plan	$200/user/month	Unlimited access to reasoning models, targets groups of 10-50 users.
Enterprise Plan	Custom pricing	Made for organizations over 50 users, offers dedicated infrastructure and priority access.

Technical Requirements: RAG systems usually need advanced setups. You must set up distributed vector databases and GPU-accelerated models. This can take a lot of resources. In contrast, Copilot works well with existing Microsoft 365 apps, making it easier to use.
Data Security and Governance: Both tools need careful thought about data security. RAG focuses on safety and governance checks, keeping sensitive information safe. Copilot also stresses governance, helping you manage who can access information.
Use Cases: Think about what your business needs. RAG is great where accuracy and compliance are very important. Copilot is best for daily productivity tasks, making it perfect for teams wanting to improve operations.

Scenarios for RAG

RAG works best in situations where accuracy and compliance are very important. Here are some cases where RAG can help a lot:

Compliance and Governance: If your business is in a regulated field, RAG helps you get accurate information. This is key for meeting rules and avoiding legal problems.
Research and Development: Use RAG when your team needs exact data from large databases. This helps in making smart decisions based on trustworthy information.
Legal and Regulatory: RAG helps legal teams by giving accurate references and citations. This is vital for preparing cases and following rules.
Data-Driven Decision Making: When your organization depends on data for decisions, RAG can boost trust in the information you get. This leads to better results and informed choices.

Scenarios for Copilot

Microsoft 365 Copilot is perfect for boosting productivity in everyday tasks. Here are some situations where Copilot can be very helpful:

Daily Operations: Use Copilot to make routine tasks easier, like writing emails, summarizing meetings, and creating reports. This saves time and lets your team focus on more important work.
Project Management: Copilot can help with planning and scheduling projects. It manages resources and timelines well, keeping your projects on track.
Data Analysis: When you need quick insights from data, Copilot can create charts and summaries. This helps you make decisions without getting stuck in complicated data work.
Collaboration: Copilot improves teamwork by giving real-time suggestions while editing documents. This feature helps raise the quality of work and encourages teamwork among members.

By knowing these scenarios, you can better match your choice of RAG or Copilot with your business goals.

To sum up, knowing about RAG and Microsoft 365 Copilot is very important for your business. Each tool has special strengths that fit different needs. RAG is great at giving accurate and trustworthy information. This makes it perfect for industries with strict rules. Copilot helps you work faster inside Microsoft 365 by making daily tasks easier.

When you think about your choices, remember future updates. RAG will probably connect better with company content. Copilot will get smarter AI search features. By looking at what your business needs, you can pick the best tool to help your company succeed. 🚀

FAQ

What is RAG used for in business?

RAG helps businesses find accurate information from big data sets. You can use it for following rules, research, and making decisions. This way, you always have reliable data when you need it.

How does Microsoft 365 Copilot enhance productivity?

Microsoft 365 Copilot makes tasks easier within Microsoft 365. It helps you write emails, summarize documents, and organize schedules. This saves time and makes you more efficient.

Can I use RAG and Copilot together?

Yes, you can use RAG and Copilot together. RAG gives you accurate data, while Copilot helps you work faster. Using both tools can improve your workflows and help with decision-making.

Which tool is better for compliance needs?

RAG is better for compliance needs. It focuses on being accurate and gives reliable information from trusted sources. This makes it great for industries with strict rules, like finance and healthcare.

Is Microsoft 365 Copilot easy to use?

Yes, Microsoft 365 Copilot is easy to use. It works well with Microsoft 365 apps, so you can boost your productivity without needing a lot of training or technical skills.

🚀 Want to be part of m365.fm?

Then stop just listening… and start showing up.

👉 Connect with me on LinkedIn and let’s make something happen:

🎙️ Be a podcast guest and share your story
🎧 Host your own episode (yes, seriously)
💡 Pitch topics the community actually wants to hear
🌍 Build your personal brand in the Microsoft 365 space

This isn’t just a podcast — it’s a platform for people who take action.

🔥 Most people wait. The best ones don’t.

👉 Connect with me on LinkedIn and send me a message:
"I want in"

Let’s build something awesome 👊

1
00:00:00,000 --> 00:00:02,200
The night was thick with static.

2
00:00:02,200 --> 00:00:06,200
Your tenant humming files stacked like rusted steel.

3
00:00:06,200 --> 00:00:10,040
You want answers fast, but not guesses.

4
00:00:10,040 --> 00:00:12,080
Copilot is quick, friendly.

5
00:00:12,080 --> 00:00:16,000
It skims your M3 and 65 streets and hands you a summary.

6
00:00:16,000 --> 00:00:21,120
Good enough for small talk, not for policy, not for risk.

7
00:00:21,120 --> 00:00:22,760
Rag cuts deeper.

8
00:00:22,760 --> 00:00:27,960
It drags truth from your own stack, sights it, stands by it.

9
00:00:27,960 --> 00:00:30,920
So here's the map when Copilot is enough.

10
00:00:30,920 --> 00:00:34,040
When you need your own pipeline and why teams blow this call,

11
00:00:34,040 --> 00:00:36,680
then pay for it in rework, tickets and trust.

12
00:00:36,680 --> 00:00:37,800
Stay sharp.

13
00:00:37,800 --> 00:00:40,840
There's a secret step that makes this 10x easier.

14
00:00:40,840 --> 00:00:43,440
We'll get there.

15
00:00:43,440 --> 00:00:48,720
Now, we define the players, defining the players,

16
00:00:48,720 --> 00:00:51,920
what is Copilot and LLMs.

17
00:00:51,920 --> 00:00:54,680
Start with the engine, large language models.

18
00:00:54,680 --> 00:00:59,280
They speak like us because they are trained on oceans of public text.

19
00:00:59,280 --> 00:01:02,440
Patterns, tokens, next word bets, they don't know.

20
00:01:02,440 --> 00:01:04,720
They predict that prediction is powerful.

21
00:01:04,720 --> 00:01:10,800
Drafts, summaries, code sketches, meeting notes cleaned and sorted.

22
00:01:10,800 --> 00:01:13,120
Fast.

23
00:01:13,120 --> 00:01:17,520
But down here, your world is narrow, specific, messy.

24
00:01:17,520 --> 00:01:21,000
HR policies with last year's date, a procurement form

25
00:01:21,000 --> 00:01:24,800
that changed last month, a device standard buried in a PDF

26
00:01:24,800 --> 00:01:27,040
on a forgotten SharePoint stack.

27
00:01:27,040 --> 00:01:30,000
A plain LLM won't see it because in this city,

28
00:01:30,000 --> 00:01:33,640
the model only knows what you feed it now, not what you hit back then,

29
00:01:33,640 --> 00:01:35,600
not what changed yesterday.

30
00:01:35,600 --> 00:01:36,520
Enter Copilot.

31
00:01:36,520 --> 00:01:40,160
Think of it like a streetwise guide inside Microsoft 365.

32
00:01:40,160 --> 00:01:43,440
It can walk outlook alleys, teams corridors, SharePoint towers,

33
00:01:43,440 --> 00:01:44,840
one drive back rooms.

34
00:01:44,840 --> 00:01:46,120
It reads what you can read.

35
00:01:46,120 --> 00:01:48,440
It stays in bounds with your permissions.

36
00:01:48,440 --> 00:01:51,800
It drafts replies, writes meeting recaps,

37
00:01:51,800 --> 00:01:55,280
pulls related files you already have rights to.

38
00:01:55,280 --> 00:01:59,240
It's good at what's in my lane right now.

39
00:01:59,240 --> 00:02:03,240
It's safe, governed, and fast because the terrain is familiar.

40
00:02:03,240 --> 00:02:05,120
Your identity controls the gates.

41
00:02:05,120 --> 00:02:07,160
Your data doesn't leave the precinct.

42
00:02:07,160 --> 00:02:08,720
Where does Copilot shine?

43
00:02:08,720 --> 00:02:10,960
Every day flow, you're buried in email,

44
00:02:10,960 --> 00:02:12,240
you need a clean summary.

45
00:02:12,240 --> 00:02:16,200
You want a quick brief for a meeting using files from your team site.

46
00:02:16,200 --> 00:02:18,120
Do you want to rephrase a doc in your voice?

47
00:02:18,120 --> 00:02:21,520
You're staying inside M365, no custom data pipelines,

48
00:02:21,520 --> 00:02:26,680
no special retrieval logic, no extra tooling, straight utility.

49
00:02:26,680 --> 00:02:29,400
But here's where most people mess up.

50
00:02:29,400 --> 00:02:35,520
They expect Copilot to know the factory floor, SOP, the onboarding maze,

51
00:02:35,520 --> 00:02:40,280
the device compliance footnote from a PDF that never made it to the right library.

52
00:02:40,280 --> 00:02:45,560
They ask it to cross check ERP fields or explain a CRM status code

53
00:02:45,560 --> 00:02:49,080
that lives outside the M365 city limits.

54
00:02:49,080 --> 00:02:51,920
Then they blame the model when the answer leans generic.

55
00:02:51,920 --> 00:02:53,160
We know better.

56
00:02:53,160 --> 00:02:54,760
It's not a mind reader.

57
00:02:54,760 --> 00:02:57,160
It's a runner working a single district.

58
00:02:57,160 --> 00:02:58,720
So what's missing?

59
00:02:58,720 --> 00:03:02,520
Retrieval, controlled, precise.

60
00:03:02,520 --> 00:03:05,640
You need a librarian who knows where the bodies are buried.

61
00:03:05,640 --> 00:03:08,240
A way to turn your PDFs, web pages,

62
00:03:08,240 --> 00:03:11,560
weekies and databases into fast, relevant context

63
00:03:11,560 --> 00:03:13,360
at the exact moment of the question.

64
00:03:13,360 --> 00:03:15,920
That's retrieval augmented generation.

65
00:03:15,920 --> 00:03:18,080
Rags, it's not a model trick.

66
00:03:18,080 --> 00:03:19,880
It's an information supply chain.

67
00:03:19,880 --> 00:03:21,520
The reason this works is simple.

68
00:03:21,520 --> 00:03:25,200
The model's memories short, prompts are finite.

69
00:03:25,200 --> 00:03:28,600
But you can fetch just the right chunks at query time.

70
00:03:28,600 --> 00:03:31,440
Feed them in, ask the model to answer only from those sites.

71
00:03:31,440 --> 00:03:34,080
You get grounded output, you get proof.

72
00:03:34,080 --> 00:03:36,320
And when your data shifts, you re-index.

73
00:03:36,320 --> 00:03:40,800
No retraining, no long cycles, just fresher truth.

74
00:03:40,800 --> 00:03:42,000
Now let's be clear.

75
00:03:42,000 --> 00:03:44,560
Co-pilot can already surface some of your files

76
00:03:44,560 --> 00:03:47,080
if they live in M365 and you have access.

77
00:03:47,080 --> 00:03:50,640
It's handy, but it won't build you a custom index

78
00:03:50,640 --> 00:03:54,440
across SharePoint, file shares, websites, and line

79
00:03:54,440 --> 00:03:56,000
of business systems.

80
00:03:56,000 --> 00:03:59,760
It won't let you tune chunk sizes for a gnarly SOP.

81
00:03:59,760 --> 00:04:03,480
It won't force citations, run retrieval evaluations,

82
00:04:03,480 --> 00:04:05,720
or give you a custom tool to hit an API

83
00:04:05,720 --> 00:04:07,640
and pull a live value mid-answer.

84
00:04:07,640 --> 00:04:08,960
That's outside its beat.

85
00:04:08,960 --> 00:04:10,320
Think constraints.

86
00:04:10,320 --> 00:04:13,400
Co-pilot is bounded by your tenant's native graph

87
00:04:13,400 --> 00:04:15,080
in its own product surface.

88
00:04:15,080 --> 00:04:18,240
That's good for speed, great for governance.

89
00:04:18,240 --> 00:04:21,960
But if you need cross-system truth, strict grounding,

90
00:04:21,960 --> 00:04:25,520
or repeatable answers tied to version sources,

91
00:04:25,520 --> 00:04:27,640
you'll feel the walls closing in.

92
00:04:27,640 --> 00:04:29,800
This clicked for me when a team asked Co-pilot

93
00:04:29,800 --> 00:04:32,480
to untangle a device hardening policy.

94
00:04:32,480 --> 00:04:34,960
The dock was split across three PDFs.

95
00:04:34,960 --> 00:04:36,000
One was stale.

96
00:04:36,000 --> 00:04:37,880
One lived on a file server.

97
00:04:37,880 --> 00:04:40,520
One had the only correct baseline.

98
00:04:40,520 --> 00:04:43,320
Co-pilot did its best with what it could see.

99
00:04:43,320 --> 00:04:44,720
The answer sounded right.

100
00:04:44,720 --> 00:04:45,760
It wasn't.

101
00:04:45,760 --> 00:04:47,960
Service desk tickets spiked.

102
00:04:47,960 --> 00:04:49,200
Minutes wasted.

103
00:04:49,200 --> 00:04:51,120
Trust bled.

104
00:04:51,120 --> 00:04:52,920
With rag, you don't pray.

105
00:04:52,920 --> 00:04:55,920
You prepare, you ingest, you chunk, you tag.

106
00:04:55,920 --> 00:04:59,320
You index with vectors, so meaning survives paraphrase.

107
00:04:59,320 --> 00:05:01,400
You fetch the closest chunks.

108
00:05:01,400 --> 00:05:02,640
You show citations.

109
00:05:02,640 --> 00:05:04,320
You add a hard rule.

110
00:05:04,320 --> 00:05:06,920
If nothing fits, say you don't know.

111
00:05:06,920 --> 00:05:08,880
Illucinations drop.

112
00:05:08,880 --> 00:05:11,200
Confidence climbs.

113
00:05:11,200 --> 00:05:15,720
If you remember nothing else, Co-pilot is your inbox partner.

114
00:05:15,720 --> 00:05:17,400
Rag is your knowledge pipeline.

115
00:05:17,400 --> 00:05:20,200
Use the guide when you're inside the district.

116
00:05:20,200 --> 00:05:23,840
Build the pipeline when the stakes demand proof.

117
00:05:23,840 --> 00:05:25,320
Defining the players.

118
00:05:25,320 --> 00:05:26,280
What is Rag?

119
00:05:26,280 --> 00:05:28,680
Retrieval augmented generation.

120
00:05:28,680 --> 00:05:30,320
Rag isn't magic.

121
00:05:30,320 --> 00:05:33,440
It's plumbing, cold pipes, hot truth.

122
00:05:33,440 --> 00:05:35,040
Three moving parts.

123
00:05:35,040 --> 00:05:38,080
Retrieval, augmentation, generation.

124
00:05:38,080 --> 00:05:39,200
Retrieval first.

125
00:05:39,200 --> 00:05:40,960
You build a private library.

126
00:05:40,960 --> 00:05:43,320
Not glossy, brutal.

127
00:05:43,320 --> 00:05:44,800
Your PDFs.

128
00:05:44,800 --> 00:05:45,840
Wikis.

129
00:05:45,840 --> 00:05:46,840
Pages.

130
00:05:46,840 --> 00:05:47,840
Tables.

131
00:05:47,840 --> 00:05:48,840
Tickets.

132
00:05:48,840 --> 00:05:50,360
Change logs.

133
00:05:50,360 --> 00:05:53,680
SOP binders that smell like dust and denial.

134
00:05:53,680 --> 00:05:55,120
You don't throw them at a model.

135
00:05:55,120 --> 00:05:56,040
You process them.

136
00:05:56,040 --> 00:05:58,760
You slice them into small, useful pieces.

137
00:05:58,760 --> 00:05:59,600
Chunks.

138
00:05:59,600 --> 00:06:02,440
Then you tag them with metadata so a machine can smell

139
00:06:02,440 --> 00:06:04,400
context like a bloodhound.

140
00:06:04,400 --> 00:06:08,480
You vectorize the chunks or meaning holds when the words don't match.

141
00:06:08,480 --> 00:06:09,800
That's the search fuel.

142
00:06:09,800 --> 00:06:11,320
Augmented next.

143
00:06:11,320 --> 00:06:13,480
A question walks in.

144
00:06:13,480 --> 00:06:14,520
Plane clothes.

145
00:06:14,520 --> 00:06:16,720
You convert the question into a vector.

146
00:06:16,720 --> 00:06:19,400
You hunt the nearest chunks in your index.

147
00:06:19,400 --> 00:06:21,480
You pull back the top few that matter.

148
00:06:21,480 --> 00:06:22,920
You package them as context.

149
00:06:22,920 --> 00:06:23,800
Not all your data.

150
00:06:23,800 --> 00:06:25,320
Just the right charts.

151
00:06:25,320 --> 00:06:28,040
Tight, relevant, dated, sourced.

152
00:06:28,040 --> 00:06:29,640
You add instructions.

153
00:06:29,640 --> 00:06:32,760
Answer only from these sites.

154
00:06:32,760 --> 00:06:34,560
Quote the source.

155
00:06:34,560 --> 00:06:36,800
If it's not here say you don't know.

156
00:06:36,800 --> 00:06:39,360
That's the leash generation last.

157
00:06:39,360 --> 00:06:41,280
Now the model speaks.

158
00:06:41,280 --> 00:06:42,480
But it's grounded.

159
00:06:42,480 --> 00:06:44,320
It's standing on your sources.

160
00:06:44,320 --> 00:06:45,800
It doesn't riff from memory.

161
00:06:45,800 --> 00:06:48,840
It reasons with the pages you fed it seconds ago.

162
00:06:48,840 --> 00:06:51,280
The answer lands with receipts.

163
00:06:51,280 --> 00:06:52,280
Citations.

164
00:06:52,280 --> 00:06:53,800
No bluffing.

165
00:06:53,800 --> 00:06:55,880
The thing most people miss.

166
00:06:55,880 --> 00:06:58,440
Rag isn't about shoving PDFs into a hungry mouth.

167
00:06:58,440 --> 00:06:59,680
It's a supply chain.

168
00:06:59,680 --> 00:07:00,680
Data in.

169
00:07:00,680 --> 00:07:02,040
Chunks clean.

170
00:07:02,040 --> 00:07:03,600
Index is tuned.

171
00:07:03,600 --> 00:07:05,160
Queries tight.

172
00:07:05,160 --> 00:07:06,680
Evaluation constant.

173
00:07:06,680 --> 00:07:08,040
Break any link.

174
00:07:08,040 --> 00:07:09,600
And the outputs rot.

175
00:07:09,600 --> 00:07:12,840
Why this beats fine tuning for business?

176
00:07:12,840 --> 00:07:14,960
Because policies move.

177
00:07:14,960 --> 00:07:16,960
S-O-P's shift.

178
00:07:16,960 --> 00:07:18,560
Fields change.

179
00:07:18,560 --> 00:07:23,040
You don't want to retrain a model every time procurement updates align.

180
00:07:23,040 --> 00:07:25,120
With rag you just fix the library.

181
00:07:25,120 --> 00:07:26,120
Raine decks.

182
00:07:26,120 --> 00:07:27,480
You keep the same engine.

183
00:07:27,480 --> 00:07:28,760
You change the fuel.

184
00:07:28,760 --> 00:07:31,320
Now how does this flow in Azure Streets?

185
00:07:31,320 --> 00:07:34,880
Azure AI Foundry gives you the scaffolding.

186
00:07:34,880 --> 00:07:40,440
You ingest from SharePoint stacks Web crawls file shares maybe databases if you map exports.

187
00:07:40,440 --> 00:07:43,480
You chunk with strategies that match the form.

188
00:07:43,480 --> 00:07:45,800
Heading's matter for S-O-P's.

189
00:07:45,800 --> 00:07:48,320
Tables need careful passing.

190
00:07:48,320 --> 00:07:53,400
You add metadata version owner, date system, then you vectorize.

191
00:07:53,400 --> 00:07:57,600
Embeddings turn text into numbers that remember intent.

192
00:07:57,600 --> 00:08:03,080
You store those vectors in Azure AI search or a vector store that plays nice.

193
00:08:03,080 --> 00:08:04,080
That's your index.

194
00:08:04,080 --> 00:08:05,080
Fast.

195
00:08:05,080 --> 00:08:06,080
Searchable.

196
00:08:06,080 --> 00:08:07,680
Ready when the question hits.

197
00:08:07,680 --> 00:08:09,920
When the question hits the retriever goes to work.

198
00:08:09,920 --> 00:08:13,840
It finds the closest matches by meaning not just keywords.

199
00:08:13,840 --> 00:08:16,040
You can do hybrid search too.

200
00:08:16,040 --> 00:08:19,960
Semantics plus text because in this city precision is survival.

201
00:08:19,960 --> 00:08:21,760
You set strictness.

202
00:08:21,760 --> 00:08:23,120
Loose finds more.

203
00:08:23,120 --> 00:08:24,440
Risks noise.

204
00:08:24,440 --> 00:08:25,440
Tight finds less.

205
00:08:25,440 --> 00:08:26,440
Boosts trust.

206
00:08:26,440 --> 00:08:29,800
Filing to your risk then you augment the prompt.

207
00:08:29,800 --> 00:08:33,240
You inject the retrieve chunks clean and labeled.

208
00:08:33,240 --> 00:08:39,400
You set rules, site sources, stay within content, no inventing.

209
00:08:39,400 --> 00:08:45,160
You pass that to the model you deployed doesn't need to be exotic just consistent.

210
00:08:45,160 --> 00:08:46,800
Now guardrails.

211
00:08:46,800 --> 00:08:48,720
You add don't know behavior.

212
00:08:48,720 --> 00:08:50,240
You cap on the length.

213
00:08:50,240 --> 00:08:53,360
You require citations to render with the output.

214
00:08:53,360 --> 00:08:55,160
You log which chunks were used.

215
00:08:55,160 --> 00:09:00,800
You track latency, hit rates and nulls because a pipeline you can't measure is a pipeline

216
00:09:00,800 --> 00:09:02,280
you can't trust.

217
00:09:02,280 --> 00:09:04,480
Common traps down here.

218
00:09:04,480 --> 00:09:06,560
Chunks too big.

219
00:09:06,560 --> 00:09:09,400
Model gets lost in the sprawl.

220
00:09:09,400 --> 00:09:13,520
Chunks too small, context shatters, no metadata.

221
00:09:13,520 --> 00:09:20,200
You can't filter stale from fresh, wrong embeddings for your language or domain.

222
00:09:20,200 --> 00:09:22,560
Retrieval returns pretty but wrong passages.

223
00:09:22,560 --> 00:09:24,160
No evaluation loop.

224
00:09:24,160 --> 00:09:26,800
Nobody checks if the top five actually answer the question.

225
00:09:26,800 --> 00:09:29,280
The game changer nobody talks about.

226
00:09:29,280 --> 00:09:30,280
Feedback.

227
00:09:30,280 --> 00:09:32,120
You let users flag bad answers.

228
00:09:32,120 --> 00:09:33,920
You fix the chunk or the source.

229
00:09:33,920 --> 00:09:35,520
You re-index.

230
00:09:35,520 --> 00:09:36,840
Quality rises.

231
00:09:36,840 --> 00:09:38,080
Trust follows.

232
00:09:38,080 --> 00:09:41,240
If you remember nothing else, remember this.

233
00:09:41,240 --> 00:09:42,480
Ragn makes the model local.

234
00:09:42,480 --> 00:09:43,800
It speaks in your dialect.

235
00:09:43,800 --> 00:09:45,200
It cites your law.

236
00:09:45,200 --> 00:09:47,400
It stops pretending.

237
00:09:47,400 --> 00:09:52,520
Because in this city answers without sources are just noise in the rain.

238
00:09:52,520 --> 00:09:55,640
The copilot advantage.

239
00:09:55,640 --> 00:09:57,640
General knowledge and speed.

240
00:09:57,640 --> 00:09:59,200
Copilot moves fast.

241
00:09:59,200 --> 00:10:00,880
That's the point.

242
00:10:00,880 --> 00:10:02,760
You're buried in noise.

243
00:10:02,760 --> 00:10:05,000
Male flooding your outlook alleys.

244
00:10:05,000 --> 00:10:07,360
Teams threads stacked like crates.

245
00:10:07,360 --> 00:10:08,360
Files you can see.

246
00:10:08,360 --> 00:10:10,120
Files you're allowed to see.

247
00:10:10,120 --> 00:10:11,560
Copilot walks that beat with you.

248
00:10:11,560 --> 00:10:13,280
It reads the room.

249
00:10:13,280 --> 00:10:15,920
Drafts a reply that sounds like you.

250
00:10:15,920 --> 00:10:19,120
Pulls three relevant docs from your team site.

251
00:10:19,120 --> 00:10:21,440
Builds a meeting brief in seconds.

252
00:10:21,440 --> 00:10:24,160
Rises a chat war into clean bullet lines.

253
00:10:24,160 --> 00:10:25,480
You don't hunt.

254
00:10:25,480 --> 00:10:26,480
You don't stitch.

255
00:10:26,480 --> 00:10:28,080
You just ship.

256
00:10:28,080 --> 00:10:30,600
Because in this city time kills.

257
00:10:30,600 --> 00:10:32,560
Copilot saves minutes per move.

258
00:10:32,560 --> 00:10:34,480
Add that up across a week.

259
00:10:34,480 --> 00:10:35,680
Across a team.

260
00:10:35,680 --> 00:10:37,160
Across a quarter you feel the lift.

261
00:10:37,160 --> 00:10:39,120
Now, the reason it's smooth.

262
00:10:39,120 --> 00:10:40,960
Identity adwares your badge.

263
00:10:40,960 --> 00:10:42,280
It respects your scope.

264
00:10:42,280 --> 00:10:44,120
It doesn't break out of the precinct.

265
00:10:44,120 --> 00:10:46,040
No awkward permissions chase.

266
00:10:46,040 --> 00:10:47,680
No custom pipes to maintain.

267
00:10:47,680 --> 00:10:49,200
No embeddings to generate.

268
00:10:49,200 --> 00:10:52,120
Rides the Microsoft graph like a subway map.

269
00:10:52,120 --> 00:10:53,200
Predictable.

270
00:10:53,200 --> 00:10:54,160
Govind.

271
00:10:54,160 --> 00:10:55,800
Quietly efficient.

272
00:10:55,800 --> 00:10:58,200
Drafting is where it shines.

273
00:10:58,200 --> 00:11:00,200
Cold email to warm intro.

274
00:11:00,200 --> 00:11:01,880
Rough notes to clean minutes.

275
00:11:01,880 --> 00:11:04,120
A messy deck turned tight.

276
00:11:04,120 --> 00:11:05,680
Rewrite in your tone.

277
00:11:05,680 --> 00:11:06,600
Fix spelling.

278
00:11:06,600 --> 00:11:07,720
Strip fluff.

279
00:11:07,720 --> 00:11:09,480
That's breakfast work for Copilot.

280
00:11:09,480 --> 00:11:11,280
It's also a decent scout.

281
00:11:11,280 --> 00:11:13,560
Show me related docs for this meeting.

282
00:11:13,560 --> 00:11:16,040
It maps your one drive and SharePoint lanes.

283
00:11:16,040 --> 00:11:18,120
It surfaces what's already in reach.

284
00:11:18,120 --> 00:11:20,400
You pick, you move.

285
00:11:20,400 --> 00:11:22,680
And here's the truth, the tourists miss.

286
00:11:22,680 --> 00:11:26,520
Sometimes you just need good enough, a passable draft,

287
00:11:26,520 --> 00:11:28,400
a summary that gets you oriented,

288
00:11:28,400 --> 00:11:31,400
a quick check of what's changed in a folder you own.

289
00:11:31,400 --> 00:11:32,720
These aren't court cases.

290
00:11:32,720 --> 00:11:34,320
They're errands.

291
00:11:34,320 --> 00:11:36,880
Copilot eats errands.

292
00:11:36,880 --> 00:11:39,520
Now, boundaries.

293
00:11:39,520 --> 00:11:43,560
Because down here in the undernet, speed can blind you.

294
00:11:43,560 --> 00:11:45,640
Copilot won't require your knowledge.

295
00:11:45,640 --> 00:11:48,600
It won't cross the fences into ERP vaults,

296
00:11:48,600 --> 00:11:53,440
or that legacy file, share the last admin sealed with tape.

297
00:11:53,440 --> 00:11:56,800
It won't enforce answer only with citations on your command.

298
00:11:56,800 --> 00:12:00,800
It won't let you tune chunk sizes or run retrieval evaluations.

299
00:12:00,800 --> 00:12:04,480
It can pull what's visible in your M365 lanes.

300
00:12:04,480 --> 00:12:06,840
Useful, but not surgical.

301
00:12:06,840 --> 00:12:08,520
So when do you stay with it?

302
00:12:08,520 --> 00:12:13,800
When the task lives in outlook, teams, SharePoint, one drive.

303
00:12:13,800 --> 00:12:18,560
When the answer is a draft, a summary, a rewrite, a quick list.

304
00:12:18,560 --> 00:12:21,000
When governance and simplicity matter,

305
00:12:21,000 --> 00:12:23,400
more than custom reach.

306
00:12:23,400 --> 00:12:27,240
When you don't need strict grounding or cross-system joins,

307
00:12:27,240 --> 00:12:30,240
I watch the PM use it to prep a vendor call.

308
00:12:30,240 --> 00:12:33,000
30 messages, four files.

309
00:12:33,000 --> 00:12:37,360
She asked for a one-page brief with open issues and decisions.

310
00:12:37,360 --> 00:12:39,520
Copilot's batted out in under a minute.

311
00:12:39,520 --> 00:12:41,440
She tweaked three lines.

312
00:12:41,440 --> 00:12:42,240
Done.

313
00:12:42,240 --> 00:12:44,240
That's the lane.

314
00:12:44,240 --> 00:12:47,880
The mistake is trying to make it a judge, a compliance oracle

315
00:12:47,880 --> 00:12:49,560
across-system agent.

316
00:12:49,560 --> 00:12:53,840
You ask it about a policy that changed last month in a PDF it can't see.

317
00:12:53,840 --> 00:12:56,480
It answers smooth, generic, and wrong.

318
00:12:56,480 --> 00:12:59,600
You won't spot the fracture until the ticket queues wells.

319
00:12:59,600 --> 00:13:00,680
We've seen that movie.

320
00:13:00,680 --> 00:13:02,280
Use the runner for what it is.

321
00:13:02,280 --> 00:13:05,520
Fast, local, polite with your time.

322
00:13:05,520 --> 00:13:08,080
Once you nail that, everything else clicks.

323
00:13:08,080 --> 00:13:09,480
You don't overreach.

324
00:13:09,480 --> 00:13:11,240
You don't over trust.

325
00:13:11,240 --> 00:13:14,160
You keep the errands light in the stakes low.

326
00:13:14,160 --> 00:13:18,360
And when the question demands proof, you switch tools.

327
00:13:18,360 --> 00:13:20,800
Because in this city speed matters.

328
00:13:20,800 --> 00:13:22,440
But truth wins.

329
00:13:22,440 --> 00:13:27,440
The rag necessity when proprietary data is king.

330
00:13:27,440 --> 00:13:29,640
Some questions wear badges.

331
00:13:29,640 --> 00:13:33,440
Prepriotary high stakes, no guesses allowed.

332
00:13:33,440 --> 00:13:36,280
That's when the librarian steps in.

333
00:13:36,280 --> 00:13:37,400
Rag.

334
00:13:37,400 --> 00:13:40,640
You've got policies outside the M365 Glow.

335
00:13:40,640 --> 00:13:44,280
Device baselines buried in stale PDFs.

336
00:13:44,280 --> 00:13:48,360
Onboarding rules have in SharePoint, half on a file server.

337
00:13:48,360 --> 00:13:51,160
S-O-P's that live as Word, Wiki, and rumor.

338
00:13:51,160 --> 00:13:53,440
Copilot can't patrol those alleys.

339
00:13:53,440 --> 00:13:55,080
Rag can.

340
00:13:55,080 --> 00:13:56,960
You build the pipeline.

341
00:13:56,960 --> 00:13:58,600
Injust the mess.

342
00:13:58,600 --> 00:14:01,560
Chunk the docs to match how people ask.

343
00:14:01,560 --> 00:14:03,040
Headings with steps.

344
00:14:03,040 --> 00:14:05,360
Tables preserved, not mangled.

345
00:14:05,360 --> 00:14:08,120
Metadata stamped owner version date system sensitivity.

346
00:14:08,120 --> 00:14:12,960
Then vectors embeddings turn language into coordinates, meaning

347
00:14:12,960 --> 00:14:15,560
survives paraphrase.

348
00:14:15,560 --> 00:14:20,720
As your AI search holds the map, fast nearest neighbor hybrid

349
00:14:20,720 --> 00:14:23,560
with semantics when keywords help.

350
00:14:23,560 --> 00:14:26,840
Now the question hits, which device hardening baseline

351
00:14:26,840 --> 00:14:30,560
applies to contractors on Mac OS Q3 revision?

352
00:14:30,560 --> 00:14:34,120
The retriever hunts nearest chunks by meaning filters

353
00:14:34,120 --> 00:14:39,480
by version equals Q3, owner equals security, region equals global,

354
00:14:39,480 --> 00:14:41,560
strictness tuned to avoid noise.

355
00:14:41,560 --> 00:14:43,560
Three passages come home.

356
00:14:43,560 --> 00:14:44,840
You package them.

357
00:14:44,840 --> 00:14:47,760
You say answer only from these sites.

358
00:14:47,760 --> 00:14:52,480
If missing say you don't know, receipts required.

359
00:14:52,480 --> 00:14:54,480
The model speaks grounded.

360
00:14:54,480 --> 00:14:55,960
It quotes the clause.

361
00:14:55,960 --> 00:14:57,040
It links the source.

362
00:14:57,040 --> 00:14:58,680
It names the revision.

363
00:14:58,680 --> 00:15:01,000
No riff, just law.

364
00:15:01,000 --> 00:15:04,360
Policy and compliance Q&A is built for this.

365
00:15:04,360 --> 00:15:06,480
Employees stop guessing.

366
00:15:06,480 --> 00:15:09,960
They stop pinging the desk for the same 12 questions.

367
00:15:09,960 --> 00:15:12,160
Citations build trust.

368
00:15:12,160 --> 00:15:14,560
If a dog is wrong, you fix the source.

369
00:15:14,560 --> 00:15:17,280
Reindex, the answer changes tomorrow.

370
00:15:17,280 --> 00:15:18,440
No retraining loop.

371
00:15:18,440 --> 00:15:19,680
That's power.

372
00:15:19,680 --> 00:15:24,840
SOPs next, manufacturing, IT operations, HR workflows.

373
00:15:24,840 --> 00:15:26,240
These aren't poems.

374
00:15:26,240 --> 00:15:28,040
Their sequences.

375
00:15:28,040 --> 00:15:30,720
Rag turns them into step-by-step guidance.

376
00:15:30,720 --> 00:15:32,640
Chunk-by-heading and step number.

377
00:15:32,640 --> 00:15:34,280
Preserve warnings.

378
00:15:34,280 --> 00:15:36,720
Include preconditions.

379
00:15:36,720 --> 00:15:41,680
At query time, retrieve the exact step and its guard rails.

380
00:15:41,680 --> 00:15:44,560
Ask the model to render a checklist, not a story.

381
00:15:44,560 --> 00:15:45,920
You get action not vibes.

382
00:15:45,920 --> 00:15:49,960
Then CRM and ERP context, Dynamics SAP Sales Force.

383
00:15:49,960 --> 00:15:52,560
Copilot can't reach the transaction guts.

384
00:15:52,560 --> 00:15:55,360
Rag can unify the narrative.

385
00:15:55,360 --> 00:15:58,200
Embed release notes, field dictionaries, integration

386
00:15:58,200 --> 00:16:02,520
wikis, add tools for live lookups, read only APIs,

387
00:16:02,520 --> 00:16:05,160
status checks, inventory pulls.

388
00:16:05,160 --> 00:16:07,960
The model retrieves the spec, calls the tool,

389
00:16:07,960 --> 00:16:10,120
and explains the result with sites.

390
00:16:10,120 --> 00:16:12,000
Now the agent doesn't invent.

391
00:16:12,000 --> 00:16:13,720
It confirms.

392
00:16:13,720 --> 00:16:16,320
This is where proprietary data rules.

393
00:16:16,320 --> 00:16:17,600
You need control.

394
00:16:17,600 --> 00:16:21,600
Control of chunk sizes and overlap, so meaning holds.

395
00:16:21,600 --> 00:16:24,800
Control of retrieval filters to lock scope.

396
00:16:24,800 --> 00:16:27,720
Control of grounding to force citations.

397
00:16:27,720 --> 00:16:32,960
Control of tools to fetch live truth and governance.

398
00:16:32,960 --> 00:16:35,080
Foundry gives you safe lanes.

399
00:16:35,080 --> 00:16:36,680
Data boundaries.

400
00:16:36,680 --> 00:16:38,840
Roll-based access.

401
00:16:38,840 --> 00:16:40,680
Versioned indexes.

402
00:16:40,680 --> 00:16:42,280
Monitored runs.

403
00:16:42,280 --> 00:16:47,520
Responsible AI hooks so you can trace why an answer said what it said.

404
00:16:47,520 --> 00:16:51,160
Leaders sleep better when the chain of custody is clear.

405
00:16:51,160 --> 00:16:54,920
Cost and complexity know the shape.

406
00:16:54,920 --> 00:17:00,160
As your AI search carries the index, tier by traffic,

407
00:17:00,160 --> 00:17:02,720
hybrid search helps accuracy.

408
00:17:02,720 --> 00:17:05,400
Embedding's cost per thousand tokens.

409
00:17:05,400 --> 00:17:07,320
Batch at ingestion.

410
00:17:07,320 --> 00:17:10,240
Re-embed only change chunks.

411
00:17:10,240 --> 00:17:14,280
Model hosting depends on traffic and context size.

412
00:17:14,280 --> 00:17:16,080
Keep prompts tight.

413
00:17:16,080 --> 00:17:18,360
Site only what's needed.

414
00:17:18,360 --> 00:17:19,840
Storage is cheap.

415
00:17:19,840 --> 00:17:21,760
Bad indexing isn't.

416
00:17:21,760 --> 00:17:25,160
Plan your fields, plan your filters.

417
00:17:25,160 --> 00:17:27,440
When is rag not optional?

418
00:17:27,440 --> 00:17:29,840
When correctness beats speed?

419
00:17:29,840 --> 00:17:33,000
When answers must side chapter and verse.

420
00:17:33,000 --> 00:17:36,800
When knowledge lives beyond M3 in 65.

421
00:17:36,800 --> 00:17:40,880
When workflows require tools to act, not just speak.

422
00:17:40,880 --> 00:17:46,040
When you need repeatability, same question, same answer, same source.

423
00:17:46,040 --> 00:17:49,680
I walked a tenant that was bleeding data, policy scattered,

424
00:17:49,680 --> 00:17:53,320
doops everywhere, teams asked co-pilot for clarity,

425
00:17:53,320 --> 00:17:57,440
it smiled and guessed, good tone, bad facts.

426
00:17:57,440 --> 00:18:00,200
Tickets stacked like bodies in the alley.

427
00:18:00,200 --> 00:18:04,480
We built the pipeline index across SharePoint and file servers,

428
00:18:04,480 --> 00:18:08,360
trash the doops, tag the truth, force citations,

429
00:18:08,360 --> 00:18:10,840
set don't know as a badge of honor.

430
00:18:10,840 --> 00:18:13,920
Service desk load dropped, trust climbed.

431
00:18:13,920 --> 00:18:18,080
Not because the model got smarter, because the library did.

432
00:18:18,080 --> 00:18:19,920
And this one matters.

433
00:18:19,920 --> 00:18:22,960
Rag is not a feature you toggle on Tuesdays.

434
00:18:22,960 --> 00:18:27,320
It's a discipline, sources owned, pipelines monitored,

435
00:18:27,320 --> 00:18:30,840
evaluations weekly, users in the loop,

436
00:18:30,840 --> 00:18:34,520
you measure retrieval hit rate, you inspect top-k quality,

437
00:18:34,520 --> 00:18:36,720
you track don't know and fix the gap.

438
00:18:36,720 --> 00:18:37,920
Quality is a habit.

439
00:18:37,920 --> 00:18:41,480
So when proprietary data runs the show, you pick the librarian,

440
00:18:41,480 --> 00:18:44,520
you build the pipes, you demand receipts.

441
00:18:44,520 --> 00:18:47,800
Because in this city, your knowledge is the currency.

442
00:18:47,800 --> 00:18:53,120
Guard it, index it, retrieve it clean, then let the model speak,

443
00:18:53,120 --> 00:18:57,880
and stand by it, case study, global manufacturing company,

444
00:18:57,880 --> 00:19:01,000
anonymized, the tenant was humming.

445
00:19:01,000 --> 00:19:04,920
A global manufacturer, plans on three continents,

446
00:19:04,920 --> 00:19:09,320
policies stacked like sheet metal, they wanted truth on demand.

447
00:19:09,320 --> 00:19:12,080
Not vibes, not guesses.

448
00:19:12,080 --> 00:19:14,840
The service desk was drowning in repeat questions,

449
00:19:14,840 --> 00:19:17,760
compliance was a rumor, documents fought each other

450
00:19:17,760 --> 00:19:21,520
in the dark, they tried going faster with generic tools,

451
00:19:21,520 --> 00:19:25,320
speed without ground, it backfired.

452
00:19:25,320 --> 00:19:30,720
So we built a librarian, private, quiet, Azure streets,

453
00:19:30,720 --> 00:19:35,720
rag as the spine, indexes with teeth, citations mandatory,

454
00:19:35,720 --> 00:19:41,040
a team's doorway, ask, get the clause, see the source.

455
00:19:41,040 --> 00:19:44,240
Confidence returned, tickets fell,

456
00:19:44,240 --> 00:19:47,640
leadership finally saw the shape of their own rules,

457
00:19:47,640 --> 00:19:51,920
and believed them before, without rag, the pain points,

458
00:19:51,920 --> 00:19:53,760
it started ugly.

459
00:19:53,760 --> 00:19:57,240
4,800 policy files scattered like rust,

460
00:19:57,240 --> 00:20:02,000
sharepoint towers, old file servers, email attachments,

461
00:20:02,000 --> 00:20:07,000
masquerading as truth, unlabeled, duplicated, stale.

462
00:20:07,000 --> 00:20:11,040
Employees walked in with the same 12 questions,

463
00:20:11,040 --> 00:20:16,040
security, devices, onboarding, travel allowances,

464
00:20:16,040 --> 00:20:21,040
12 to 15 hits a day on the desk every day.

465
00:20:21,040 --> 00:20:26,040
Each one costing five to seven minutes of hunt and pack search,

466
00:20:26,040 --> 00:20:31,040
keyword roulette, open a PDF, skim, hope the date isn't lying,

467
00:20:31,040 --> 00:20:35,040
open the twin, different wording, which one wins?

468
00:20:35,040 --> 00:20:36,040
Nobody knew.

469
00:20:36,040 --> 00:20:38,040
Copilot helped in the shallow lanes.

470
00:20:38,040 --> 00:20:42,040
It could find what the employee already had rights to in M365,

471
00:20:42,040 --> 00:20:45,040
it summarized, it drafted, it saved seconds,

472
00:20:45,040 --> 00:20:49,040
but down here the signal lived outside the glow,

473
00:20:49,040 --> 00:20:52,040
the correct baseline sat in a PDF on a file share,

474
00:20:52,040 --> 00:20:55,040
the update lived in a wiki the team forgot to publish,

475
00:20:55,040 --> 00:20:59,040
a meeting note contradicted both, people asked,

476
00:20:59,040 --> 00:21:03,040
the system guessed, nice tone, bad facts,

477
00:21:03,040 --> 00:21:08,040
the fallout, errors in the field, wrong device hardening steps,

478
00:21:08,040 --> 00:21:13,040
onboarding detours, policy exceptions issued on the wrong revision,

479
00:21:13,040 --> 00:21:16,040
the service desk became referee and archaeologist,

480
00:21:16,040 --> 00:21:20,040
trust bled out in small cuts, the cost wasn't just minutes,

481
00:21:20,040 --> 00:21:23,040
it was rework repeat tickets and risk,

482
00:21:23,040 --> 00:21:26,040
and every fresh hire learned a bad truth,

483
00:21:26,040 --> 00:21:29,040
finding policy was slower than ignoring it,

484
00:21:29,040 --> 00:21:32,040
that's how tenants bleed quietly in the paperwork alleys,

485
00:21:32,040 --> 00:21:37,040
no scandal, just drag, after, with Azure Rags solution,

486
00:21:37,040 --> 00:21:40,040
the transformation, we turned on a light,

487
00:21:40,040 --> 00:21:44,040
all policy and SOPs flowed into Azure AI search,

488
00:21:44,040 --> 00:21:49,040
no magic, just discipline, crawl, share point,

489
00:21:49,040 --> 00:21:52,040
sweep the file servers, stage the sources,

490
00:21:52,040 --> 00:21:56,040
chunk each document by heading in clause, preserve tables,

491
00:21:56,040 --> 00:22:01,040
tag every shard with owner, version, effective date,

492
00:22:01,040 --> 00:22:06,040
system, sensitivity, then embeddings,

493
00:22:06,040 --> 00:22:10,040
vectors that remember meaning when words change,

494
00:22:10,040 --> 00:22:14,040
hybrid search, wired for speed and precision,

495
00:22:14,040 --> 00:22:18,040
the librarian woke up, a team's agent became the doorway,

496
00:22:18,040 --> 00:22:21,040
employees asked the same questions,

497
00:22:21,040 --> 00:22:25,040
the retriever hunted by meaning, then filtered by version and owner,

498
00:22:25,040 --> 00:22:28,040
top passages returned with receipts,

499
00:22:28,040 --> 00:22:31,040
we wrapped the prompt with hard rules,

500
00:22:31,040 --> 00:22:33,040
answer only from these sites,

501
00:22:33,040 --> 00:22:37,040
quote the source, if missing, say you don't know,

502
00:22:37,040 --> 00:22:40,040
the model spoke like a clerk with a case file,

503
00:22:40,040 --> 00:22:43,040
concise, grounded two seconds, not seven minutes,

504
00:22:43,040 --> 00:22:45,040
load on the desk dropped by a third,

505
00:22:45,040 --> 00:22:47,040
not because answers were flashy,

506
00:22:47,040 --> 00:22:49,040
because they were consistent,

507
00:22:49,040 --> 00:22:51,040
contradiction surfaced as alerts,

508
00:22:51,040 --> 00:22:54,040
two PDFs claiming different bass lines,

509
00:22:54,040 --> 00:22:58,040
flagged, owners notified, fix the library,

510
00:22:58,040 --> 00:23:02,040
rain decks, tomorrow's answers aligned,

511
00:23:02,040 --> 00:23:06,040
no retraining loop, no waiting on model updates,

512
00:23:06,040 --> 00:23:09,040
just fresher truth, people trusted the machine again,

513
00:23:09,040 --> 00:23:12,040
not because it was smart, because it was verifiable,

514
00:23:12,040 --> 00:23:14,040
every answer carried a source,

515
00:23:14,040 --> 00:23:17,040
the agent didn't bluff, it opted out when blind,

516
00:23:17,040 --> 00:23:20,040
that small honesty turned users into partners,

517
00:23:20,040 --> 00:23:23,040
they reported gaps, we patched sources,

518
00:23:23,040 --> 00:23:27,040
the librarian got sharper, the city got quieter,

519
00:23:27,040 --> 00:23:31,040
credibility boosters, why rag wins on trust and accuracy,

520
00:23:31,040 --> 00:23:33,040
here's the thing most leaders miss,

521
00:23:33,040 --> 00:23:36,040
speed without proof is theatre,

522
00:23:36,040 --> 00:23:39,040
in policy work, tone isn't truth,

523
00:23:39,040 --> 00:23:44,040
rag forces receipts, citations aren't a nice to have,

524
00:23:44,040 --> 00:23:46,040
they're the contract,

525
00:23:46,040 --> 00:23:49,040
when the answer links to clause 4.3,

526
00:23:49,040 --> 00:23:52,040
revision Q3 owned by security,

527
00:23:52,040 --> 00:23:56,040
the debate ends, people stop arguing with each other,

528
00:23:56,040 --> 00:24:00,040
they argue with the source, and that's fixable,

529
00:24:00,040 --> 00:24:03,040
the biggest win wasn't speed, it was accuracy,

530
00:24:03,040 --> 00:24:06,040
you'll hear that line from the floor,

531
00:24:06,040 --> 00:24:08,040
because once the librarian stands up,

532
00:24:08,040 --> 00:24:11,040
employees stop second guessing the clerk at the window,

533
00:24:11,040 --> 00:24:13,040
they click the source, they see the date,

534
00:24:13,040 --> 00:24:16,040
they move with confidence, that's how you erase

535
00:24:16,040 --> 00:24:18,040
the quiet drag that kills quarters,

536
00:24:18,040 --> 00:24:22,040
users trusted the answers more because citations were mandatory,

537
00:24:22,040 --> 00:24:24,040
trust isn't about personality,

538
00:24:24,040 --> 00:24:26,040
it's about auditability,

539
00:24:26,040 --> 00:24:30,040
mandatory citations make every response traceable,

540
00:24:30,040 --> 00:24:32,040
it also makes QA measurable,

541
00:24:32,040 --> 00:24:36,040
you can test retrieval, did the top passages actually answer the question?

542
00:24:36,040 --> 00:24:39,040
If not, fix chunks or tags,

543
00:24:39,040 --> 00:24:43,040
evaluate again, quality climbs,

544
00:24:43,040 --> 00:24:47,040
the IT department didn't need to retrain a single model,

545
00:24:47,040 --> 00:24:50,040
just structured their data,

546
00:24:50,040 --> 00:24:54,040
that line matters to budgets, fine tuning sounds heroic,

547
00:24:54,040 --> 00:24:57,040
it's also slow and brittle for policy work,

548
00:24:57,040 --> 00:25:00,040
policies evolve, SOPs shift,

549
00:25:00,040 --> 00:25:02,040
with RAAG the engine stays put,

550
00:25:02,040 --> 00:25:04,040
the fuel changes,

551
00:25:04,040 --> 00:25:06,040
rain decks changed chunks,

552
00:25:06,040 --> 00:25:08,040
keep embedding current,

553
00:25:08,040 --> 00:25:10,040
no six week model cycles,

554
00:25:10,040 --> 00:25:13,040
no vendor lock to a training pipeline,

555
00:25:13,040 --> 00:25:15,040
you can't control,

556
00:25:15,040 --> 00:25:17,040
and governance rocks steady.

557
00:25:17,040 --> 00:25:21,040
Azure AI Foundry gives you lanes.

558
00:25:21,040 --> 00:25:23,040
Identity through Entra,

559
00:25:23,040 --> 00:25:25,040
role-based access,

560
00:25:25,040 --> 00:25:27,040
data stays in the tenants shadow,

561
00:25:27,040 --> 00:25:29,040
versioned indexes,

562
00:25:29,040 --> 00:25:31,040
monitoring on latency,

563
00:25:31,040 --> 00:25:33,040
hit rate, nulls, citations,

564
00:25:33,040 --> 00:25:37,040
you can show a chain of custody from question to source,

565
00:25:37,040 --> 00:25:42,040
responsible AI hooks carry the paperwork you need when someone asks,

566
00:25:42,040 --> 00:25:44,040
why did it say that?

567
00:25:44,040 --> 00:25:48,040
In short, RAAG doesn't pretend to know it proves what it knows,

568
00:25:48,040 --> 00:25:50,040
that's why it wins.

569
00:25:50,040 --> 00:25:52,040
Choosing your AI strategy,

570
00:25:52,040 --> 00:25:54,040
here's the map in one line,

571
00:25:54,040 --> 00:25:58,040
Copilot is the runner for your M365 streets,

572
00:25:58,040 --> 00:26:01,040
RAAG is the librarian for your law,

573
00:26:01,040 --> 00:26:03,040
use the runner for drafts, summaries,

574
00:26:03,040 --> 00:26:06,040
and quick pulls inside the district.

575
00:26:06,040 --> 00:26:09,040
Bring the librarian when correctness, citations,

576
00:26:09,040 --> 00:26:11,040
and cross-system truth matter.

577
00:26:11,040 --> 00:26:14,040
If you're ready to build that pipeline, subscribe,

578
00:26:14,040 --> 00:26:18,040
then watch the next episode where we blueprint a minimal RAAG flow,

579
00:26:18,040 --> 00:26:20,040
costs and guardrails.

580
00:26:20,040 --> 00:26:22,040
Make the call, pick the lane, move.

Mirko Peters

Founder of m365.fm, m365.show and m365con.net

Mirko Peters is a Microsoft 365 expert, content creator, and founder of m365.fm, a platform dedicated to sharing practical insights on modern workplace technologies. His work focuses on Microsoft 365 governance, security, collaboration, and real-world implementation strategies.

Through his podcast and written content, Mirko provides hands-on guidance for IT professionals, architects, and business leaders navigating the complexities of Microsoft 365. He is known for translating complex topics into clear, actionable advice, often highlighting common mistakes and overlooked risks in real-world environments.

With a strong emphasis on community contribution and knowledge sharing, Mirko is actively building a platform that connects experts, shares experiences, and helps organizations get the most out of their Microsoft 365 investments.

How to Choose Between RAG and Microsoft 365 Copilot