RAG or fine-tuning: which one answers questions about your codebase?

For most teams, retrieval is the right first step for questions about their own codebase. Code changes daily, and an index can be rebuilt in minutes, while a fine-tuned model has to be retrained. Fine-tuning earns its place when you need a consistent style or format, not fresh facts.

What each approach actually changes

Retrieval leaves the model alone. At question time, a search step finds relevant files or functions and pastes them into the prompt. The model answers from what it was just shown.

Fine-tuning changes the model's weights. You show it many examples, and it adjusts. Afterwards the knowledge is baked into the model, but you cannot easily point at where a given fact came from, and you cannot remove one fact without training again.

The original retrieval-augmented generation paper frames this as two kinds of memory. Knowledge in the weights is parametric memory. Knowledge in an external store the model can look up is non-parametric memory. The authors note that models relying only on their weights struggle to precisely access and manipulate knowledge, and that giving provenance and updating knowledge are open problems for them. Pairing the two is their answer.

Why code favours retrieval

Three properties of source code push toward retrieval.

First, it changes constantly. A function renamed this morning should be answered correctly this afternoon. With an index you re-embed the changed files. With fine-tuning you wait for a training run.

Second, answers need exact names. A model that "roughly remembers" a function signature is worse than useless. Retrieval puts the real text in front of it.

Third, access differs by person. Not everyone may read every repository. Permissions can be applied at the retrieval step. They cannot be applied inside a trained model.

When fine-tuning is the better tool

Fine-tuning fits when the problem is behaviour rather than knowledge. Examples: answering in a fixed house format, following an internal naming convention in generated code, or producing a strict structured output. These patterns are stable, so retraining is rare.

It can also help when you want a small model to do one narrow job well, such as classifying commit messages, so you do not pay for a large model on every call. We cover the mechanics in what LoRA is and why it is cheap.

A simple decision guide

• The question is "what does our code do or contain?" Use retrieval.
• The question is "write it the way we always write it?" Try prompting with examples first, then fine-tuning.
• The data is private and access varies by user? Retrieval, with permission checks.
• The data changes weekly or faster? Retrieval.
• You need citations back to files? Retrieval.
• You need a small, cheap model for one repeated task? Fine-tuning.

Combining them is common and sensible. A lightly tuned model that handles your output format, fed by retrieval that supplies the facts, covers both needs. For the security trade-offs of each, see fine-tuning versus grounding for security work.

A caution on data

Whatever you pick, private code is now flowing into an index or a training set. OWASP's guidance on sensitive information disclosure recommends scrubbing sensitive content before it enters training and limiting model access to external data sources. Treat the index as a copy of your code with the same access rules.

Quick check

Sources

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• OWASP LLM02:2025 Sensitive Information Disclosure

Last reviewed: 19 October 2026.