Building a RAG Chat System: Lessons from DIFINES AI

Retrieval-Augmented Generation (RAG) has become the default pattern for domain-specific chat systems.

Instead of fine-tuning a model on your data, you retrieve relevant documents at query time and let the LLM answer from that context.

It sounds simple. In practice, the gap between a working demo and a useful assistant is mostly about knowledge quality, retrieval accuracy, and UX expectations.

Why RAG instead of a plain chatbot

For most product teams, RAG is the right starting point because:

• Answers stay grounded in your own documentation
• You can update knowledge without retraining a model
• It is cheaper and faster to ship than custom fine-tuning
• You control what the model is allowed to see

The trade-off: your chat quality is only as good as your retrieval pipeline and source material.

Case study: DIFINES AI

While working on the DIFINES AI consultant, the goal was straightforward — help users understand the DIFINES ecosystem without reading through long docs manually.

The stack:

• Knowledge source: Markdown documentation
• Embeddings: Gemini embedding-001
• Vector store: Supabase pgvector in PostgreSQL
• Retrieval: Similarity search over chunked documents
• Fallback: Global search when no relevant knowledge base results are found
• Generation: Groq Llama 3.3 70B via AI SDK
• Frontend: React chat UI aligned with the existing DIFINES landing page

The ingestion flow was: chunk markdown files → generate embeddings → store in pgvector → retrieve top matches on each user question → pass context to the LLM.

When vector search returns no useful match, the system falls back to global search so users can still get an answer instead of hitting a dead end.

This worked well for a focused domain assistant where answers should come from verified project documentation first — with a broader fallback when the knowledge base does not cover the question.

What worked well

Markdown as the knowledge base

Simple to maintain, version in Git, and easy for non-engineers to update.

pgvector inside Supabase

One database for application data and vector search means less infrastructure to manage early on.

Fast inference with Groq

Low-latency responses matter in chat UX. Users expect near-instant replies, not long waits.

Scoped domain

RAG performs best when the assistant has a clear boundary rather than trying to answer everything.

Grounded answers

When retrieval works, responses stay tied to actual documentation instead of relying on generic model knowledge.

Global search fallback

Pure RAG breaks down when the knowledge base has no useful match. A fallback path improves coverage without forcing the model to guess from weak context.

Testing with real user questions

Real questions surfaced retrieval gaps and edge-case phrasing much earlier than curated demo prompts.

What was hard

Retrieval quality is the real product

Bad chunking or weak embeddings can give the model irrelevant context while the final answer still sounds confident.

Stale knowledge

If documentation changes but embeddings are not regenerated, the assistant can quietly become outdated.

Chunk size trade-offs

Too small loses context. Too large creates noisy retrieval and increases token usage.

No retrieval means weak answers

A strict RAG-only system can fail when nothing relevant exists in the knowledge base. This is where fallback design becomes important.

Evaluation still takes iteration

Similarity scores alone do not tell you whether an answer is actually useful. Retrieved chunks and final responses still need real evaluation.

What I would prioritize next time

• Show whether an answer came from internal docs or global search
• Re-ingest automatically when documentation changes
• Log retrieved chunks and fallback triggers per query
• Keep expanding evaluation cases from real user questions
• Keep the assistant scope narrow and explicit

Final thoughts

RAG chat systems are not really about choosing the best LLM.

They are about document structure, retrieval design, and honest UX.

DIFINES AI showed that a lightweight stack — Markdown, pgvector, fallback search, and fast inference — can deliver real value when the domain is clear and the knowledge base is maintained.

If you are building a RAG chatbot today, spend less time obsessing over model selection and more time on chunking, ingestion, retrieval quality, fallback behavior, and testing with real users.