How to Build a Reliable RAG System with Complex Documentation

RAG (Retrieval-Augmented Generation) sounds simple—until you’re dealing with real enterprise data.

Especially in industries like manufacturing, energy, or finance, where documentation is:

• Messy PDFs with tables, images, and footnotes

• Charts embedded in scanned pages

• Technical manuals with nested sections and hyperlinks

• Highly regulated and accuracy-critical

If you think you can just “embed the documents and call it a day,” you’re in for a world of hallucinations.

Here’s a real-world playbook to make your RAG system work—without it breaking or misleading users.

1. Know Your Document Types

Start by centralizing your documentation: One repo, folder, or storage layer.

Then classify the file types:

• PDFs (text-based vs scanned)

• Markdown or HTML

• Images or charts

• Tables inside Word/Excel files

Understanding what you’re working with dictates everything—from parsers to chunking logic.

2. Chunk Size Matters (More Than You Think)

Most teams default to small chunks (200 tokens) because it "feels safe."

But in our tests, smaller chunks actually increase hallucination risk—especially when dealing with technical, interdependent data (e.g., formula + explanation on the next page).

💡 Aim for 250–500 token chunks where possible.

You want enough context, but not so much that retrieval becomes noisy.

3. Chunking Cuts: End of Sentence or Paragraph Only

Avoid cutting chunks mid-sentence or mid-table.

Otherwise, your model will pull half a thought—then confidently lie about the rest.

Use paragraph breaks, punctuation, or heading tags (<h2>, ###, etc.) as natural chunk boundaries.

4. Add Context Overlay

If your documents are complex, context bleeding helps.

Include a 100–150 token “overlay” from the previous chunk into the next one.

Why?

It gives the LLM continuity across chunk boundaries—and that’s critical when explaining diagrams or comparing data in tabular sections.

5. Use the Right Tools for the Job

Not all data is text.

To extract meaningful chunks, you need the right tooling:

Pro tip: Run test chunks through each and compare embeddings + output relevance.

6. Remove Useless Docs

Just because you can embed something doesn’t mean you should.

If a document:

• Has no clear business value

• Adds noise (e.g., old templates, duplicates)

• Is never cited in useful outputs

Delete it.

You’re just polluting the retrieval process.

7. Monitor the Full Flow

Most teams debug the input/output.

But that’s not enough.

You need end-to-end visibility:

• What chunk was retrieved?

• What document was it from?

• Did the answer cite the right chunk?

• Where did hallucination happen?

Use trace logs or RAG pipelines with observability layers (e.g., Langfuse, Trulens).

8. Build for Trust (Especially in Regulated Environments)

Users don’t trust AI by default.

Especially not in energy, healthcare, or legal contexts.

You must:

• Cite exact document + page number (or deep link)

• Show source text alongside answers

• Flag uncertainty when confidence is low

💡 This isn’t just for user peace of mind—it’s often required for compliance.

9. Optimize Based on Business Use, Not Dev Curiosity

RAG systems don’t improve by adding more GPUs.

They improve when the output solves real problems.

Watch end users:

• Are answers helpful?

• Are they acting on the output?

• Are they asking the same thing twice?

Prioritize fixes around business value, not engineering neatness.

TL;DR – The Reliable RAG Checklist

• Store & classify document types

• Chunk by meaning, not character count

• Add overlay context

• Use the right parsing stack

• Remove noise

• Monitor the full flow

• Build user trust with citations

• Iterate based on business feedback

Want Help With Your Own RAG System?

We offer a free RAG audit:

We’ll analyze your document types, test retrieval patterns, and give you a custom strategy to deploy a production-ready RAG assistant in weeks—not months.

▶ Schedule your session