---
name: rag-vs-fine-tuning
description: Use when deciding how to make a model perform better on domain-specific knowledge or tasks.
---

# RAG vs Fine-Tuning Decision

## Overview

RAG and fine-tuning are not interchangeable knobs. **RAG injects retrievable facts at inference time**; **fine-tuning changes model behavior and style in the weights**. Pick wrong and you pay in retraining cycles, stale knowledge, or brittle prompts.

## When to Use

- Domain knowledge or tone is missing from base-model behavior
- Team is debating "should we embed our docs" vs "should we train on our data"
- Skip when few-shot prompting already clears your eval bar on real inputs

## Quick Reference

| Need | Lean toward |
|---|---|
| Facts change often; citations required | RAG |
| Stable format, tone, or task behavior | Fine-tuning or strong system prompt |
| Both grounded facts and house style | RAG + prompt; add PEFT only if prompt plateaus |
| Tiny static FAQ | RAG or prompt; fine-tune is usually overkill |
| Classifier with stable labels | Fine-tune or structured prompt + eval |

## Implementation

1. **Classify the gap** — is it *what the model knows* or *how it responds*?
2. **Check data shape** — RAG needs a retrievable corpus; fine-tuning needs representative labeled pairs.
3. **Check change frequency** — monthly policy updates favor RAG; stable style favors fine-tune.
4. **Prototype the cheaper path first** — RAG + eval fixtures before any training job.
5. **Measure on held-out real queries** — not playground vibes.
6. **Document the decision** — so the next engineer does not re-litigate it.

## On This Portfolio

The public assistant uses RAG over published writings and research — content changes when I publish in admin. Job-fit classification is a candidate for fine-tuning only if prompt-only approaches plateau; general Q&A stays retrieval-first.

## Common Mistakes

- Fine-tuning to memorize facts that will change next quarter
- Using RAG to fix JSON format or routing (that is prompting / schema validation)
- Skipping evals because "the demo looked good"

## Related Skills

- Data availability audit
- Prompt engineering vs fine-tuning tradeoff
- RAG pipeline design