July 16, 2026

RAG on Azure — Simply Explained

RAG on Azure — Simply Explained

Retrieval-Augmented Generation (RAG) is one of the most important techniques behind modern AI applications, enabling large language models to answer questions using your own business data instead of relying only on their training knowledge. In this episode of Microsoft Knowledge Nuggets, we explain RAG on Azure in plain English and show how organizations build secure, accurate, and trustworthy AI solutions with Microsoft Azure.

You'll learn how the RAG process works—from ingesting documents and creating vector embeddings to storing them in a vector database and retrieving the most relevant information for every user query. We explain key concepts including vector search, embeddings, chunking, semantic search, Azure AI Search, Azure OpenAI, and grounding AI responses with your organization's private knowledge. You'll also discover why RAG dramatically reduces hallucinations while providing more accurate, up-to-date, and source-aware answers.

The episode also explores real-world use cases such as AI chatbots, internal knowledge assistants, customer support, document search, and enterprise copilots. We discuss how Azure services like Azure AI Search, Azure OpenAI, Azure AI Foundry, and Azure Storage work together to build scalable, secure, and production-ready AI applications that keep sensitive business data under your control.

Whether you're an Azure developer, AI engineer, cloud architect, IT professional, or simply curious about enterprise AI, this episode provides a practical introduction without unnecessary complexity. By the end, you'll understand why RAG has become the foundation of modern AI applications and how Azure makes it possible to build intelligent systems that deliver accurate, context-aware answers using your own data.

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Season 3 Cloud Architecture Simply Explained

Retrieval-Augmented Generation (RAG) represents a groundbreaking approach in the realm of artificial intelligence. RAG on Azure enhances your AI solutions by improving how systems retrieve and generate information. This method integrates external data sources to ensure that the responses are not only accurate but also relevant to your specific queries. Recent studies show that RAG improves data retrieval accuracy through hybrid queries, which combine both keyword and vector searches. This approach leads to contextually accurate results, significantly reducing hallucination rates in AI-generated content.

Key Takeaways

  • RAG combines information retrieval with generative AI, improving the accuracy and relevance of responses.
  • Integrating external data sources allows RAG to provide real-time, evidence-based answers, crucial for fields like healthcare.
  • RAG enhances AI performance, reducing customer support response times by 40-50%, leading to higher satisfaction.
  • The architecture of RAG on Azure includes key components like retrievers and generators, ensuring efficient data retrieval.
  • Implementing RAG can lead to a 25-30% reduction in operational costs, offering a strong return on investment.
  • RAG's flexibility allows it to adapt to various business needs, supporting different content types and indexing options.
  • Using RAG in customer support can resolve issues 47% faster, improving first-contact resolution rates significantly.
  • Emerging trends in RAG include real-time data integration and multimodal capabilities, enhancing its future applications.

Understanding RAG

Definition of RAG

Retrieval-Augmented Generation (RAG) combines information retrieval with generative AI to enhance the quality of responses. It allows AI systems to access relevant data from external sources before generating answers. This approach ensures that the information you receive is accurate and contextually relevant. Here are some formal definitions of RAG from reputable sources:

Source Definition
IBM RAG adds an information retrieval component to the AI workflow, gathering relevant information and feeding that to the generative AI model to enhance response quality and utility.
ServiceNow RAG enhances large language models by incorporating data from external knowledge bases to improve the accuracy and relevance of outputs without retraining.
NVIDIA RAG is a technique for enhancing the accuracy and reliability of generative AI models with information fetched from specific and relevant data sources.
Microsoft Azure RAG combines two techniques: retrieving information from sources and using that information to inform the generation of responses.

Importance in AI

RAG plays a crucial role in the advancement of AI solutions. By integrating external data sources, RAG enhances the accuracy and relevance of AI-generated responses. This capability is particularly significant in fields like healthcare, where accurate information can impact decision-making. For instance, RAG provides evidence-based answers by accessing the latest research and clinical guidelines, which is vital for accurate diagnoses.

  • RAG enhances AI accuracy by integrating external data sources, leading to more reliable responses.
  • In a study, RAG-enhanced models achieved 99.0% accuracy in interpreting hepatologic disease guidelines, significantly outperforming traditional models.
  • RAG systems, like IBM Watson, matched expert recommendations in 96% of cancer diagnosis cases, demonstrating their effectiveness in real-world applications.

RAG vs. Traditional Models

RAG differs significantly from traditional AI models in several ways. Traditional models rely solely on pre-trained data, which can become outdated. In contrast, RAG employs a two-step data retrieval process. It generates sub-queries to fetch relevant information, allowing for more nuanced responses. Here are some key differences:

  • RAG integrates retrieved data with its pre-trained knowledge, while traditional models may provide outdated answers.
  • RAG's architecture utilizes transformers and attention mechanisms to focus on relevant text parts, enhancing response coherence compared to traditional models.
  • RAG can provide responses informed by the most current data, making it ideal for applications requiring up-to-date information.

By leveraging both pre-trained knowledge and external data, RAG improves performance on complex queries. This adaptability allows RAG to stay current without retraining, unlike traditional models that require updates to their knowledge base.

RAG on Azure: How It Works

RAG on Azure: How It Works

Retrieval-Augmented Generation (RAG) on Azure operates through a well-defined architecture that enhances AI capabilities. Understanding this architecture helps you grasp how RAG integrates various components to deliver accurate and relevant responses.

Architecture Overview

The architecture of RAG on Azure consists of several key components that work together seamlessly. Here’s a breakdown of these components:

  • Retriever: This component searches external data sources for relevant information to enhance your query response.
  • Generator: The large language model (LLM) combines your original query with the retrieved context to produce accurate answers.
  • Vector Database: This database stores data as embeddings, which aids the retriever in finding relevant information efficiently.
  • Document Ingestion and Chunking: This process involves splitting raw documents into manageable chunks for effective retrieval.
  • Embedding Generation: This step converts document chunks into vectors for comparison with your queries.
  • Retrieval Layer: This layer matches your input against stored vectors to retrieve the most relevant chunks.
  • Prompt Construction and Generation: This final step merges your query with the retrieved text to create a structured context for the response.

Data Flow Process

The data flow in RAG on Azure follows a systematic approach. First, the retriever identifies relevant data from various sources. Next, the generator uses this data along with your query to create a response. This process ensures that the information you receive is not only accurate but also contextually relevant.

Integration with Azure AI Search

Integrating RAG with Azure AI Search enhances data retrieval capabilities significantly. Here’s how this integration benefits you:

  • Enhanced Control: RAG aligns responses with specific enterprise content, ensuring that the information is tailored to your needs.
  • Improved Relevance: Incorporating Azure AI Search leads to more accurate responses, as it retrieves vetted sources.
  • Flexibility: The integration supports various content types and indexing options, making it adaptable to different business requirements.

To implement RAG effectively, consider the following best practices:

  1. Ingestion and Indexing: Ensure that your data is properly ingested and indexed for optimal retrieval.
  2. Retrieval: Regularly evaluate the retrieval process to maintain accuracy.
  3. Generation: Continuously refine the generation process to enhance response quality.

By following these practices, you can maximize the effectiveness of your RAG on Azure implementation.

Technical Requirements

Deploying RAG architecture on Azure requires specific components. Here’s a summary of the essential services:

Service/Component Description
Azure OpenAI Service Provides access to large language models for processing data.
Azure AI Search Enables retrieval of relevant information from business data sources.
Microsoft Fabric Supports data integration and management for RAG systems.
Azure Machine Learning Facilitates model training and deployment for RAG applications.
Azure Blob Storage Stores large datasets that can be accessed by RAG systems.
SharePoint integrations Allows for the retrieval of enterprise data from SharePoint.
Microsoft Graph API Provides access to Microsoft 365 data for enhanced context in responses.

By understanding the architecture and integration of RAG on Azure, you can leverage its capabilities to enhance your AI solutions effectively.

Benefits of RAG on Azure

Benefits of RAG on Azure

Enhanced Data Retrieval

RAG on Azure significantly improves data retrieval processes. By integrating external data sources, you can access accurate and relevant information quickly. Organizations using RAG report a 40% faster information discovery rate. This speed enhances your ability to make informed decisions. Additionally, RAG reduces operational costs by 25% to 30%. You can expect a return on investment within 6 to 12 months of deployment.

RAG also addresses common data retrieval challenges in enterprise environments. It allows you to:

  • Access enterprise knowledge securely
  • Respect existing permissions and governance policies
  • Provide real-time responses
  • Reduce hallucinations in AI-generated content
  • Improve operational efficiency
  • Support compliance requirements

By grounding AI responses in authoritative data, RAG enhances the accuracy of your AI systems. This capability reduces hallucinations and increases confidence in AI-generated answers.

Improved AI Performance

RAG enhances AI performance by allowing systems to utilize real-time data. This integration leads to quicker and more accurate responses. Companies using RAG report a 40-50% reduction in customer support response times. This improvement boosts customer satisfaction significantly. Furthermore, internal efficiency improves, with teams spending up to 30% less time on manual document searches.

RAG also streamlines decision-making processes. AI agents can reference actual support documentation, leading to prompt and precise responses. In finance, RAG pulls relevant historical data for accurate forecasting. Legal teams can reference contracts effectively, ensuring compliance. Enhanced search relevance improves user satisfaction and engagement by up to 20%.

Scalability and Flexibility

RAG on Azure offers remarkable scalability and flexibility for large-scale AI deployments. Azure Container Apps and Azure Kubernetes Service (AKS) enable seamless scaling of AI workloads. You can deploy RAG solutions on-premises or at the edge, supporting dynamic IoT applications. This flexibility is crucial for organizations with strict data residency requirements.

Azure AI Foundry Local allows developers to run RAG workflows on-premises or at the edge. Deploying RAG at the edge on AKS clusters running on Azure Local empowers you to leverage generative AI capabilities while maintaining data sovereignty. This adaptability ensures that your AI applications can grow alongside your business needs.

Practical Use Cases of RAG

Customer Support Automation

RAG on Azure transforms customer support by providing accurate and timely responses. AI assistants leverage RAG systems to retrieve information from various sources, enhancing the overall support experience. Here are some real-world examples of how RAG improves customer support:

Example Description
AI Assistants RAG systems help AI assistants pull relevant information from multiple sources.
Customer Support These systems enhance customer support by delivering precise answers quickly.
Overall Impact Organizations see improved customer satisfaction and reduced support workloads.

Implementing RAG leads to measurable outcomes. For instance, AI-assisted agents resolve issues 47% faster, achieving a 25% higher first-contact resolution rate. Companies like Vanguard report a 12% accuracy boost for support representatives using RAG-powered tools. This efficiency allows teams to focus on more complex issues, improving service quality.

Internal Knowledge Bots

RAG also enhances internal knowledge bots, which provide employees with quick access to precise, contextually relevant data. This capability boosts productivity by reducing the time spent on data gathering and analysis. Here are some benefits of using RAG in internal knowledge bots:

  • Quick access to accurate information enhances employee productivity.
  • Employees can focus on actionable insights rather than searching for data.
  • Teams automate time-consuming tasks, leading to efficient decision-making.

RAG-powered chatbots ground their responses in company documents, eliminating AI 'hallucinations.' This ensures that the information provided is trustworthy and accurate. For example, customer support chatbots retrieve information from support documents and FAQs, enhancing user satisfaction.

AI-Powered Automation Tools

RAG on Azure significantly enhances various AI-powered automation tools across industries. Here are some examples of applications that benefit from RAG:

  1. Customer Support Agents: AI chatbots utilize RAG to provide accurate responses based on policy documents.
  2. Healthcare Knowledge Assistants: Applications assist healthcare practitioners by retrieving relevant medical literature.
  3. Legal Document Analysis: Tools help legal professionals summarize and extract information from legal documents.
  4. Educational Platforms: Systems generate tailored lesson plans and quizzes based on student progress.
  5. Enterprise Search and Decision Support: Solutions help employees access relevant reports and metrics for decision-making.
  6. Personalized E-Commerce Assistants: AI tools enhance shopping experiences by retrieving product information and recommendations.

In the legal and compliance sector, RAG significantly improves contract analysis and eDiscovery processes. It can reduce manual contract review time by over 80%, enabling legal teams to conduct faster due diligence on extensive data rooms.

By leveraging RAG, organizations can improve decision-making, enhance customer support, and increase productivity. This technology provides accurate, up-to-date information, allowing businesses to respond quickly to market changes.

The Future of RAG in AI

Emerging Trends

As you look ahead, several emerging trends will shape the future of RAG in AI. These trends enhance the capabilities of RAG systems and expand their applications across various industries. Here are some key trends to watch:

  • Multimodal Retrieval and Generation: RAG systems will increasingly integrate various data types, such as text, images, and videos. This integration enhances their ability to process complex datasets, making responses richer and more informative.
  • Real-Time Data Integration: The demand for real-time data will grow, especially in industries like finance and news. RAG systems will need to incorporate live data feeds to provide timely and relevant information.
  • Ethical Considerations and Bias Mitigation: As RAG systems become more prevalent, there will be a stronger focus on transparency and equity. Developers will work to address biases in retrieval processes, ensuring fair and accurate outputs.

Potential Challenges

While RAG offers many advantages, it also faces challenges that you should consider. Scaling RAG solutions on Azure can present hurdles, particularly during peak usage times. For example, an e-commerce platform may struggle to update user reviews quickly during a sale due to overwhelming data volume. Here are some challenges to keep in mind:

  • Data Complexity: Enterprises often deal with diverse data types and formats. Identifying the right files and versions can be challenging.
  • Data Consistency: Ensuring that all data formats are consistent and compatible is crucial for effective analytics.
  • Security Protocols: Balancing data accessibility with stringent security measures is essential to protect sensitive information.

To overcome these challenges, organizations must implement efficient indexing and parallel processing. This approach can help mitigate issues related to data ingestion scalability.

RAG's Role in Next-Gen AI Solutions

RAG is poised to play a significant role in the next generation of AI solutions. By integrating information retrieval with large language models (LLMs), RAG enhances response relevance and accuracy. Here’s how RAG will influence future AI developments:

  • RAG allows for control over grounding data, ensuring responses are tailored to specific domain knowledge.
  • It improves user experience by generating more accurate and targeted responses.
  • The flexibility of modern information retrieval systems enables RAG to be applied across diverse content types.

Experts predict that RAG will enhance user confidence and satisfaction by providing accurate, up-to-date AI-generated responses. This capability allows users to make timely decisions based on the most current data available. Additionally, RAG reduces hallucinations typical of LLMs by connecting to reliable knowledge bases, improving transparency and personalization.

In summary, RAG significantly enhances AI solutions on Azure. You gain accurate and relevant responses by integrating external data sources. This approach improves data retrieval and reduces the risk of hallucinations in AI-generated content. As you explore RAG, consider its potential to transform customer support, internal knowledge management, and automation tools. The future of RAG looks promising, with advancements in real-time data integration and multimodal capabilities. Embrace RAG to elevate your AI applications and drive better outcomes for your organization.

FAQ

What is RAG on Azure?

RAG on Azure combines retrieval and generation techniques to enhance AI solutions. It allows you to access real-time data, improving the accuracy of responses generated by large language models.

How does RAG improve data retrieval?

RAG enhances data retrieval by integrating Azure AI Search. This integration allows you to fetch relevant information quickly, ensuring that your responses are accurate and contextually relevant.

Can RAG be used for customer support?

Yes, RAG can significantly improve customer support. By retrieving information from various sources, it enables AI assistants to provide accurate answers quickly, enhancing customer satisfaction.

What are the key components of RAG architecture?

The key components of RAG architecture include the retriever, generator, vector database, and embedding generation. These elements work together to ensure efficient data retrieval and response generation.

How does RAG differ from traditional AI models?

RAG differs from traditional models by integrating real-time data retrieval with generative capabilities. This approach allows you to receive up-to-date information, unlike traditional models that rely solely on pre-trained data.

Is there a tutorial available for implementing RAG on Azure?

Yes, you can find various tutorials and guides online that provide step-by-step instructions for implementing RAG on Azure. These resources help you understand the architecture and integration processes.

What is a search index in RAG?

A search index in RAG refers to the organized structure that stores data for efficient retrieval. It allows the retriever to quickly access relevant information based on your queries.

How scalable is RAG on Azure?

RAG on Azure is highly scalable. You can deploy it on Azure Container Apps or Azure Kubernetes Service, allowing you to adjust resources based on your organization's needs.

🎧 Listen to this episode

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  • Understand the key concepts behind RAG on Azure
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Today's topic is one that almost everyone has heard of, but almost nobody can explain in

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plain English.

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Ragh, retrieval, augmented, generation.

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You've seen the acronym everywhere, Microsoft announcements, AI conference talks, blog posts,

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but what does it actually mean?

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And more importantly, why should you care?

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Here's the thing.

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Large language models like GPT-4O are powerful.

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They can write essays, summarize documents, even generate code.

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But they come with two big problems.

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First, their knowledge is frozen in time, ask chat GPT about something that happened yesterday

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and it has no idea.

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Second, these models never saw your company's internal data, your policies, product details,

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customer emails.

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So when you ask an LLM a question about your business, it's guessing.

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So that's where retrieval, augmented, generation comes in.

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Ragh isn't a new model.

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It's not a bigger or better GPT.

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It's a different approach entirely.

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Instead of asking the LLM to pull answers from its training memory, Ragh first looks up

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the right information from your own data and hands that information to the model alongside

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the question.

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The model then uses that context to give you answers grounded in real up-to-date content,

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not guesses.

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Without Ragh, your AI works from memory that's months or years old.

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With Ragh, it works from your actual documents right now.

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That's the difference between an AI that sounds confident but might be wrong and one that

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shows you exactly where it got its answer.

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Here's what we'll cover today.

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First we'll look at the two problems that make Ragh necessary.

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Then we'll break down what Ragh actually is, the retrieval, the augmentation, the generation.

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Next we'll walk through the key Azure services.

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Azure AI search, Azure Open AI and Azure AI Foundry.

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And we'll finish with a real example so you can see how the pieces fit together.

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Let's start with the problem itself.

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Once you see that, the solution becomes obvious.

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The two problems LLMs have.

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So what's wrong with large language models?

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Two things actually.

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First their knowledge is frozen.

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Think about it.

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When GPT-40 was trained, it learned from a snapshot of the internet that's now months

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or years old.

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So it doesn't know about new products, recent regulations, or yesterday's news.

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It's got about a policy change from last week and it has no way to know that information

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simply isn't in its training data.

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Second, LLMs never saw your private data, your internal documents, customer emails, proprietary

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databases.

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The model has zero access to any of that.

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So when an employee asks about your specific business processes, the model can't answer.

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It doesn't know what you know.

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Some people think fine tuning solves this.

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You take the base model and train it a bit more on your own data.

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But fine tuning has its own problems.

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It's expensive, you're paying for GPU time and engineering effort.

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It doesn't fix staleness.

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Once you fine tune, the model is still frozen at that point in time.

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And it can't handle data that changes frequently like inventory levels or pricing.

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Fine tuning is like teaching someone a textbook by heart.

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Useful, but they still can't look up today's newspaper.

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The real solution is simpler.

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Give the model access to your knowledge at the moment it needs to answer.

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Instead of trying to cram everything into its memory, don't make it memorize everything.

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Let it look things up in real time, from your actual data.

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That's what Ragn does, simple idea, but it changes everything.

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What Ragn actually is.

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So here's Ragn in one sentence.

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Instead of asking the model to pull an answer from its training data, we first go and find

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the relevant information, then feed that to the model alongside the question.

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That's the core idea.

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Three simple steps.

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Retrieve, augment, and generate.

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Let me give you an analogy.

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Think of a librarian.

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You walk into a librarian, ask a question.

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You wouldn't expect the librarian to have memorized every book, right?

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That would be ridiculous.

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Instead you ask them to find the right book, open it to the right page, and read you the answer.

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The librarian doesn't memorize a thing.

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They retrieve first and read second.

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And that's exactly what Ragn does.

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Retrieve will first, generation second.

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So here's how it actually works in practice.

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The user asks a question.

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The system then searches a knowledge base.

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Your documents, your data, your private information.

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Whatever it finds gets fed into the large language model and the model generates an answer

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based on that context.

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And here's the part that matters.

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The LLM never sees your raw data.

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It only sees what the retrieval system hands it.

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It's not a drawback, so it's a security feature.

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Your sensitive documents stay in your own storage.

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At query time, the model only gets the relevant snippets it needs to answer that specific question.

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No training on your data, no memorization, no leakage.

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Your data stays where it belongs.

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So Ragn solves both problems we talked about earlier.

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Stale data?

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Not an issue.

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Your knowledge base stays current.

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Private data?

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The model only sees what you choose to retrieve.

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It's like giving the librarian access to your filing cabinet, but only letting them pull

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out the one folder you need right when you need it.

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What do you think about your data?

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What do you think about the data?

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What do you think about the data?

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What do you think about the data?

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What do you think about the data?

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What do you think about the data?

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It handles two main tasks and lets walk through them.

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First, indexing, that's getting your data ready to be searched, second, searching.

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That's actually finding the right content when someone asks a question.

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Let's go through the indexing process first.

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You start by pointing Azure AI search at your data source,

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which could be a blob storage container full of PDFs,

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a SQL database with customer records or any number of other sources.

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The service reads those documents, breaks them into smaller pieces called chunks,

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creates vector embeddings for each chunk,

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using that embedding model we talked about earlier,

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and stores everything in a search index.

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Think of the index as a giant, highly organized catalog.

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Every piece of content gets a unique ID, a chunk of text,

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and a set of coordinates on that map of meaning.

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Now the searching process.

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When a user asks a question,

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AI search converts that question into a vector using the same embedding model,

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then scans the index to find chunks whose coordinates are closest to the questions coordinates.

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It returns the top matches, usually the five to ten most relevant ones,

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and does all of this in milliseconds.

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You get a few strong options here.

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Pure vector search finds content by meaning,

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so similar concepts show up even if the exact words don't match.

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Keyword search works like a traditional search engine, finding exact word matches.

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Or you can use hybrid search, which combines both approaches,

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and that's usually the best choice.

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There's also something called semantic re-ranking,

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which takes those initial results and resorts them based on how well they actually answer the question.

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It's like a second pass that catches things the first one might have missed.

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One thing you should know.

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Azure AI search can get expensive.

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The basic tier starts around $75 a month and climbs from there.

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If you're just experimenting, use the free tier,

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or make sure you delete your resources when you're done.

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For production use though, the cost usually pays off.

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You're buying speed and reliability at scale.

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Azure Open AI plus AI Foundry, Generation and Orchestration.

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So now your data is indexed and ready for retrieval,

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but the LLM still needs to understand your question and generate a solid answer.

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That's where Azure Open AI and Azure AI Foundry come in.

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Azure Open AI provides the actual language model,

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GPT-40, GPT-4.1, or whatever model you choose.

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This is the Generation Engine.

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It receives a prompt that includes both the original question from the user

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and the context that Azure AI search retrieved.

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The model reads through that context and produces an answer based on what it finds.

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Now you can't just dump everything in and hope for the best.

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You need a system prompt, a set of instructions you give the model upfront about how to behave.

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You tell it to use only the provided context to cite its sources.

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And most importantly to say, I don't know if the relevant information isn't in the retrieve documents.

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That's what keeps the model honest.

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Without a good system prompt, the model might fall back on its training data or worse, make something up.

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So how do you actually wire all these pieces together?

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That's where Azure AI Foundry comes in.

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It's the portal where you create a project, deploy your models, connect your data sources and test your agent.

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Think of it as the control center for your entire rag setup.

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You can add your Azure AI search index as a knowledge source to your agent without writing custom code for the basic setup.

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You just create a connection, select your index and the agent can start querying it automatically.

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The portal handles the orchestration.

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When a user asks a question, the agent knows to call AI search, get the relevant chunks and pass them to the LLM along with the original question.

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Here's a concrete example.

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In one tutorial I came across, someone built a movie recommendation agent using a Netflix data set.

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They uploaded a CSV file with movie titles, descriptions and cast information to Azure Blob Storage, then used Azure AI search to index that data.

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In Azure AI Foundry, they created an agent with GPT-4O and connected the AI search index.

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When a user asked, "I like thrillers, give me some recommendations," the agent searched the index, found thriller movies from that specific data set, and returned recommendations.

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And here's the key.

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It only recommended movies that were actually in that data set.

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It didn't invent anything, didn't pull from its training data and worked exclusively from the content they provided.

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That's the power of Ragn.

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Classic vs. AgenteGrag.

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Now let's talk about setting this up.

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There are two main patterns, classic Ragn and AgenteGrag, which one you pick depends on what you're building.

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Classic Ragn is the straightforward approach.

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One query goes in, one search runs, and one answer comes out.

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Simple, fast, predictable.

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Think of it like walking up to the reception desk in an office building and asking for one specific file.

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The receptionist grabs it, hands it to you, done.

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Classic Ragn works great for factual Q&A on a single topic.

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Like, what's the company policy on remote work? Or show me the troubleshooting steps for this error code?

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It's reliable, cheap, and easy to fix when something goes wrong because the flow is linear.

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AgenteGrag is different. The AI agent decides how to retrieve information.

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It can ask multiple subquestions, search different indexes, iterate on results, and synthesize across sources before answering.

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It's like having a building manager who coordinates with different departments to gather all the pieces.

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They don't just grab one file.

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They talk to HR, check records, cross-reference policies, and then come back with a complete answer.

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Microsoft ran their own tests and found AgenteGrag gives about a 40% improvement in answer relevance for complex questions.

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That's significant, but it uses 3 to 10 times more tokens and adds latency.

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For simple questions, that extra work is wasted.

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Classic Ragn handles those just fine.

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So when do you use each?

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Classic for routine, high volume queries where speed and cost matter most.

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AgenteG for multi-step ambiguous or cross-domain questions where accuracy is worth the extra compute.

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Connection section end to end example. Let's see how all these pieces fit together with a real example.

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Imagine you're building a customer support agent for a travel agency called Marygold Travels.

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You have trip brochures, hotel reviews, and policy documents scattered across your organization.

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You want customers to ask questions and get accurate answers based on your actual content, not generic guesses.

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Step 1. Store all that content as your blob storage.

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Brochures in one folder, reviews in another, policies in a third, just files in a container.

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Step 2. Use Azure AI search to index that data by running the import data wizard,

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pointing it at your blob storage and letting it work.

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The service reads every document, chunks them into pieces, creates vector embeddings, and builds a searchable index.

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The whole process takes a few minutes.

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Step 3. Go into Azure AI Foundry and create an agent.

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Deploy GPT-4.0 as your model, write a system prompt that tells it to use only the provided context and site its sources,

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then connect your Azure AI search index as a knowledge source.

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A few clicks and it's done.

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Now a customer asks, what's the best review hotel in Dubai under $300?

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The agent takes that question and sends it to Azure AI search.

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Azure AI search converts it to a vector and finds the most relevant chunks from your reviews and brochures.

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Those chunks get passed to GPT-4.0 along with the original question.

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The model reads through them, identifies the top match, and generates a response with citations pointing back to your source documents.

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Here's the thing, the LLM never memorizes that data.

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It never trained on your hotel reviews, it works from your specific content every single time, add new reviews tomorrow and the answers update automatically.

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Remove an outdated brochure and it stops showing up.

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The model doesn't need retraining, it just needs the right retrieval layer.

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So how do you actually start building Ragnar Azure?

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Let's break it down into three steps.

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First things first, know your data.

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Before you even pick a service, figure out where your content lives, how it's organized, and who should have access.

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Ragnar lives or dies on the quality of your data.

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Next, keep it simple, don't overcomplicate the setup.

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Use the import data wizard in Azure AI search to index just one set of documents.

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Test with basic queries. Make sure your retrieval works perfectly before you add the LLM.

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Most Ragn failures aren't model problems, they're retrieval problems.

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Last step, add the LLM. Once your search is returning the right results, then you layer in generation.

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That's when you connect as you open AI and write your system prompt.

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Here's the thing, Ragnar isn't something you buy, it's a pattern you build.

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Azure gives you the pieces, storage, search, models, and the glue to connect them.

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You just need to put it all together the right way.

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If this helped you see the big picture, hit subscribe for more play in English breakdowns.

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Drop a comment with your Ragnar use case.

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I'd love to know what you're building.

Mirko Peters Profile Photo
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.