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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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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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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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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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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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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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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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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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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.