How RAG Transforms Your AI into an Expert on Your Business

Introduction

You're using a consumer AI (ChatGPT, Mistral Le Chat, Gemini, etc.) and you're amazed by its capabilities? There's good reason to be.

But have you tried asking it questions about your company's latest financial report or an internal procedure? As soon as the sources aren't public, it's powerless.

AI unable to answer questions about internal company documents

The Problem: An AI That Knows Everything... Except You

AIs like ChatGPT are trained on astronomical amounts of public data. They're experts on the world, but not on your world.

For an AI to be truly useful internally, it needs access to:

• your internal documents
• your HR policies
• your business processes
• your tools (CRM, ERP, drive)
• your databases

In short: everything that makes up your company's DNA.

The Limits of the "Copy-Paste" Approach

Manually adding documents to ChatGPT might seem like a quick and pragmatic solution (if we set aside confidentiality issues).

But in practice:

• it's time-consuming
• you don't always know which files are actually relevant
• you send too many documents or files that are too large, making the AI less effective
• you forget documents or don't use the right versions
• it doesn't integrate information from a CRM or drive

Result: internal knowledge is never complete or structured.

The Context Window: The Achilles' Heel of Our Favorite AIs

AI has a very powerful working memory... but limited. This is its context window: it can only process a certain amount of information at once.

Concrete, Quantified Limits

Today, depending on the models, this window generally ranges between 400,000 and 1 million tokens. At best, 1,500 pages.

That seems like a lot. But if you send 10 documents of 300 pages, you've already far exceeded this context.

➡️ Well beyond what an AI can digest at once.

And the more you send it, the less precise it becomes. It forgets passages or gets the context wrong. It's better to "pre-sort" what you send so that only relevant information reaches it.

This is Where RAG Comes In: The Solution for an "Intelligently Informed" AI

RAG (Retrieval Augmented Generation) is an approach where:

1. The tool connects to your sources and pre-processes documents for future searches
2. For each query, it searches for relevant "chunks" of documents ("Retrieval")
3. It sends the AI the query enriched with these relevant documents ("Augmented")
4. The AI then generates an accurate response based on these elements ("Generation")

It's like adding an ultra-fast and ultra-precise librarian assistant to your AI.

A fast and precise library

Note that RAG does not modify the model. The LLM stays the same: what changes is what you give it to read before it answers.

RAG and LLM: Who Does What in the Chain?

People often talk about RAG LLM as if it were a single technology. They are in fact two distinct building blocks, and knowing which does what avoids most design mistakes.

The LLM (Large Language Model) is the generator. It understands the question, reasons over what it is given, and writes the answer. It knows nothing about your company and has no access to your systems.

RAG is the layer around it. It prepares your documents, retrieves the useful passages at question time, and places them in the LLM's context before calling it.

In a RAG chain, every call to the LLM unfolds like this:

Three practical consequences follow.

The LLM learns nothing from your documents. It reads them on every request, then forgets them. That is good news for confidentiality, and it means an update to your sources is taken into account immediately, with no retraining.

RAG quality is decided mostly outside the LLM. If step 2 returns the wrong passages, no model will save the day: it will write a fluent, wrong answer. In our projects, most relevance gains come from chunking, retrieval and filtering, not from the choice of model.

You can swap the LLM without rebuilding the RAG. That is the point of a multi-model architecture: the same document foundation can be served by a fast, cheap model for simple questions and by a stronger one for complex analysis.

Which LLM Should You Choose for a RAG?

The answer is not "the biggest model available". Five criteria actually matter:

• Instruction following. A good RAG LLM sticks to the excerpts provided and says it does not know when the information is missing. A brilliant but talkative model that fills the gaps from general knowledge is a poor choice.
• Citation ability. Tying each statement back to the excerpt it came from is a skill that varies widely between models.
• Context window. Large enough for a dozen excerpts plus conversation history, but no more: beyond that, precision degrades and cost climbs.
• Cost and latency. An internal assistant queried a hundred times a day has a different economic profile from a monthly batch analysis.
• Language and jurisdiction. Quality varies by language, and where requests are processed is a criterion in its own right as soon as the documents are sensitive. We cover that trade-off on our sovereign AI page.

In practice, a disciplined mid-range model outperforms a poorly framed top-tier one on an enterprise RAG. The LLM is a replaceable component; your document chain is not.

Diving into the RAG Engine: How Does It Really Work?

To go further, check out our detailed article on RAG.

Step 1: Intelligent Chunking

First, your documents must be converted into usable text:

• extraction of paragraphs
• retrieval of text from tables
• interpretation of diagrams, charts, etc. (this part is important, as they often contain useful and exploitable information)

Then comes chunking. A good chunk must be:

• short → to fit in the context window
• coherent → one clear idea per block
• isolated → no mixing of concepts

You don't give the entire document to the AI. You give it the paragraph that exactly answers the question.

Step 2: The Search Mechanism (The Magic of Embeddings)

To compare texts, RAG transforms:

• your question
• your chunks

...into numerical vectors: these are embeddings. Two chunks close in meaning → their vectors are close.

Two Essential Types of Search

1. Semantic Search (by meaning)

Ideal for conceptual questions: "How do I request time off?"

2. Keyword Search (by precision)

Essential for specific terms without universal meaning:

• "Product reference XYZ-123"
• "Procedure PNC-V4"
• "Jean-Pierre Dubreuil"

Because an AI doesn't know Jean-Pierre Dubreuil from the accounting department...

➡️ The two mechanisms are complementary. A purely "semantic" RAG (which is nevertheless common) will fail on this type of search.

---

Simplified RAG Workflow

Simplified RAG workflow

Not All RAGs Are Created Equal: The Art of Choosing the Right Solution

The market is full of RAGs that are "quick to integrate," but... there are good ones and bad ones.

What makes the difference:

• Quality of conversion → not knowing how to extract data from a chart = losing important information
• Quality of chunking → chunks too large or incoherent = confused AI
• Choice of embedding models → they don't all excel in the same domains
• Hybrid search → semantic alone = errors on proper nouns

A bad RAG can make the AI vague, approximate, unable to find information that's actually present.

Another crucial point is the integration of complementary tools. If your need is only internal search, a RAG is enough. But generally, you'll also want to search the Web, query database data, trigger actions, etc. Integrating these tools multiplies the power of your AI.

RAG is Engineering

There are clearly best practices and "generic" choices that work quite well. But there's no magic recipe.

Each company has specific formats and data that require particular treatments to achieve good performance.

The Ask This Guy (ATG) Approach: The Best of RAG, Without the Hassle

At Ask This Guy, we've spent hundreds of hours benchmarking, testing, breaking, and then improving RAG pipelines.

We Offer:

• Optimized Conversion and Chunking

Numerous connectors, management of complex documents, tables, slides, images, etc.

• Intelligent Hybrid Search

Semantic + keywords for maximum precision.

• Advanced Personalization

We adapt conversions to your documents and can develop specific algorithms. We can use an embedding model adapted to your business.

• Total Flexibility

You keep control of your data. And if you want to internalize the solution one day, you get back your specific developments.

• One RAG for All Your Use Cases

Deploy the same intelligence across multiple interfaces: admin console, chatbots on your website, widget integrated into your SaaS product, API for your internal tools, etc.

• Software that combines multiple tools, not just RAG
  • search in your drives
  • web search
  • integration with your internal tools
  • advanced document processing

The Goal: an AI that knows your company as well as an experienced employee.

Conclusion: Give Voice to Your Enterprise Data with RAG

RAG isn't just a technology: it's a document intelligence strategy.

It transforms a generic AI into a true internal expert, capable of:

• answering questions precisely
• synthesizing complex documents
• streamlining processes
• aiding decision-making

If you want an AI that truly understands your company...

➡️ RAG is essential.

➡️ And we can help you implement it.

Ready to transform how your company uses its data?

Book a quick demo!