How RAG Systems Really Work — and Why They Matter for Your AI Agent

Retrieval Augmented Generation

Retrieval-Augmented Generation (RAG) is the technique that powers most advanced enterprise AI agents today. It allows your AI assistant to deliver grounded, context-aware answers — not hallucinations or vague guesses.

At EONIQ, we use RAG extensively in almost all of our Agents — to read coding guidelines or product documentation for our Specification Agent or the relevant contracts for our Claims Checking Agent – RAG is almost everywhere. But what seems a little bit like “magic” on the surface is, in fact, a carefully tuned system with many moving parts and potential pitfalls.

In this post, we’ll break down:

How RAG works (in plain terms)
Where things often go wrong
What design choices we make when building your agent

What Is RAG, Really?

RAG stands for Retrieval-Augmented Generation. It’s a two-step architecture:

Retrieval: The system finds relevant chunks of data or documents based on your query.
Generation: A language model (like GPT) uses that retrieved data as context to answer your question or complete a task based on these document chunks.

This means your agent isn’t just using what it “knows” from training — it’s actively pulling from your documentation, tickets, PDFs, or databases before answering.

Core Components of a RAG System

1. Document Ingestion & Preprocessing

PDFs, Word files, emails, database exports — your source material.

Must be cleaned, de-duplicated, and parsed into text reliably.

Challenge: Bad formatting, poor OCR, or over-stylized layouts can break retrieval quality. Images and tables, embedded inside the documents require different processing.

2. Chunking

Raw text is split into chunks (usually 200–1000 tokens).

Chunk size and overlap affect both recall (can we find what we need?) and coherence (is it understandable to the model?).

Tradeoff: Too small = fragments with no meaning. Too large = noisy, redundant, or expensive.

Speciality: Parent-child chunks is a method, where the retrieval part is done on smaller child chunks and the associated larger parent chunks are provided to the context.

3. Embedding

Each chunk is turned into a numerical vector using an embedding model (e.g., OpenAI, Cohere, Azure, or open-source models).

The Embeddings capture semantic meaning — e.g., “raise an invoice” ≈ “create a bill” and the choice of the right model is key.

4. Vector Store (Index)

Embeddings are stored in a vector database like FAISS, Weaviate, Qdrant, or Pinecone.

This index powers fast similarity search: when a query is made, it returns chunks “closest” in meaning.

5. Retrieval Logic

This is where things get interesting:

Do you retrieve the top 3 most similar chunks, or expand based on document type?

Do you re-rank using a second model? Filter based on metadata?

Should the system boost or penalize long passages, duplicates, outdated sources?

6. Prompt Construction

The retrieved chunks are assembled into a structured prompt for the LLM, which includes:

The original user query
Instructions (e.g., “Only use the context provided”)
Additional context (e.g., user role, company terminology)
The retrieved chunks in a way that the llm knows their origin and context

7. Generation & Post-Processing

The LLM responds — ideally using only the retrieved knowledge. The system may include logic to:

Highlight sources used and provide their reference
Indicate uncertainty
Trigger follow-up questions

Where RAG Systems Go Wrong

Despite its strengths, RAG can misfire — especially when systems are quickly built or poorly maintained. Here are common pitfalls:

Bad source material: A great agent starts with great documents. If your PDFs are outdated, fragmented, or full of jargon, the agent won’t magically fix that.
Wrong chunking strategy: Too much or too little context creates confusion for the model.
Poorly chosen embedding models: Some work better in legal or technical domains; others excel at general language. Picking the wrong one = irrelevant retrievals.
Naive similarity search: Sometimes the top match is semantically close — but not actually relevant to the user’s intent. That’s why re-ranking and filtering matter.
Lack of traceability: Users don’t trust agents that “just say things.” Without visible sources or explanations, the output loses credibility.

How We Build Better RAG-Based Agents

We don’t just use RAG — we tune it for your domain, your documents, and your use case.
Here’s what we do differently:

Custom document preprocessing – Sometimes it is important to customize the document processing to extract tables, headers, and metadata to fullfil the task at hand — not just the raw text.
Chunk tuning – If necessary for the specific task, we analyze sample queries to find the optimal chunking strategy per document type. Here the EONIQ Evaluation Center is key to perform mass testing.
Embedding model selection – Depending on your language, format, and industry, we benchmark multiple embedding providers.
Retrieval refinement – We use hybrid strategies — combining semantic similarity with keyword filters, document types, and access control.
Trust-first generation – Every agent shows its working: where the answer came from, what document was used, and what logic applied.

Why It Matters for You

It is fundamentally important that the raw data is well read and accessible for your Agent, as it needs to understand your content deeply — and use it responsibly.

A well-designed RAG system means:

Accurate, grounded answers
Almost no hallucination
Embedded domain knowledge
Traceability and trust

A poorly designed one?

You get vague responses, frustration, and failed adoption.

Final Thought

RAG is not magic — it’s a system. One that requires careful engineering, clean content, and thoughtful tuning.

At EONIQ, we build custom AI Agents that don’t just work — they work on your knowledge, your rules, and your terms.