Turning a Generic LLM into a Ruby Expert: What RAG Fixed and What It Didn’t

Turning a Generic LLM into a Ruby Expert: What RAG Fixed and What It Didn’t

June 4, 2026

A practical look at hallucinations, retrieval, and why having the right documentation is not the same as understanding it.

Over the past few months, I’ve been experimenting with a simple question:

Can a generic LLM become a Ruby expert simply by giving it access to Ruby documentation?

The answer is both yes and no.

Like many developers exploring AI tooling, I built a Retrieval-Augmented Generation (RAG) pipeline using a local vector database and indexed Ruby documentation. The goal was straightforward: reduce hallucinations and improve technical accuracy when answering questions about Ruby libraries and APIs.

The results were fascinating.

The model improved dramatically.

But it didn’t stop hallucinating.

Instead, the hallucinations evolved.

The Assumption Most Developers Make

When first learning about RAG, many developers assume a workflow like this:

Question
    ↓
Retrieve relevant documentation
    ↓
Provide context to LLM
    ↓
Correct answer

It feels logical.

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Turning a Generic LLM into a Ruby Expert: What RAG Fixed and What It Didn’t – Linking Ruby knowledge from the most remote places in the world.

Turning a Generic LLM into a Ruby Expert: What RAG Fixed and What It Didn’t June 4, 2026 A practical look at hallucinations, retrieval, and why having the right documentation is not the same …

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