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Serhii Kalyna
Serhii Kalyna

Posted on • Originally published at kalyna.pro

RAG vs Fine-Tuning: When to Use Which (Developer's Guide)

#machinelearning #ai #python #tutorial

If you're building an LLM-powered application, you'll hit this question quickly: should I use RAG (Retrieval-Augmented Generation) or fine-tune the model? Both approaches customize LLM behavior — but they solve different problems.

What Is RAG?

RAG retrieves relevant documents at inference time and injects them into the prompt. The model stays unchanged — you're giving it fresh context per query.

import anthropic
from your_vector_db import search  # Chroma, Pinecone, etc.

client = anthropic.Anthropic()

def rag_answer(question: str) -> str:
    docs = search(question, top_k=5)
    context = "\n\n".join(docs)

    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": f"Context:\n{context}\n\nQuestion: {question}"
        }]
    )
    return response.content[0].text
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When RAG works well:

  • Your knowledge base changes frequently (docs, tickets, product updates)
  • You need to cite sources or show evidence
  • You have a large corpus that won't fit in context
  • You want to avoid hallucinations on factual queries

What Is Fine-Tuning?

Fine-tuning continues training on a dataset of examples, updating the model's weights so it learns a new style, format, or domain.

# Fine-tuning is done via the provider's API or training pipeline.
# For open-source models, use Axolotl or Unsloth:

from transformers import AutoModelForCausalLM, Trainer, TrainingArguments

model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
# prepare dataset, define training args, run Trainer
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When fine-tuning works well:

  • You need a specific output format (JSON schema, markdown template, code style)
  • You want the model to adopt a consistent persona or tone
  • You have 1,000+ high-quality labeled examples
  • Inference latency from long prompts is a bottleneck

Side-by-Side Comparison

Dimension RAG Fine-tuning
Knowledge source External documents at runtime Baked into weights at train time
Updates Instant — just update your DB Requires retraining
Hallucination risk Lower (grounded in retrieved docs) Higher
Data needed Any documents 500–10,000+ labeled examples
Cost Vector DB + extra tokens GPU compute or API fine-tune fee
Latency Slightly higher (retrieval step) Same as base model
Best for Factual Q&A, documentation, support Style, format, specialized tasks

Decision Framework

Use RAG if:

  • Your data changes more than once a month
  • You need answers grounded in specific documents
  • You don't have labeled input→output pairs

Use fine-tuning if:

  • You want a consistent output format every time
  • You have thousands of curated examples
  • The task is about style/tone, not factual recall
  • You've already tried prompt engineering and it's not enough

Use both if:

  • You need domain-specific output format (fine-tuning) AND up-to-date facts (RAG)
  • Example: a customer support bot that answers in a specific template using fresh product docs

Quick Decision Test

  1. Is the answer in a document I own? → RAG
  2. Is the task about format/style, not knowledge? → Fine-tuning
  3. Do I have 1,000+ labeled examples? → Fine-tuning is viable
  4. Does my data change weekly? → RAG (fine-tuning won't keep up)

Practical Starting Point

For most developer projects, start with RAG. It's faster to build, easier to update, and gives you explainable results. Fine-tune only after you've validated that RAG alone can't meet your quality bar.

The best LLM applications often combine both: fine-tune for reliable output structure, add RAG for fresh knowledge.

Originally published at kalyna.pro

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