RAG vs fine-tuning: which one to choose for your use case

When an LLM needs knowledge specific to your company — manuals, processes, products, policies — you have two main paths: RAG (Retrieval-Augmented Generation) or fine-tuning. Choosing wrong costs time, money and accuracy.

What RAG is, in one sentence

RAG retrieves the most relevant fragments of your knowledge base at query time and injects them into the prompt. The LLM doesn't learn your information — it looks it up every time, like a person with the manual in hand.

What fine-tuning is, in one sentence

Fine-tuning modifies the model's weights by training it on your examples. The knowledge is "baked" into the model — it doesn't need to look it up in real time.

A practical comparison

When to use RAG

RAG is the right answer in most business cases. Choose RAG when:

• Your information changes frequently (products, prices, policies, versioned manuals)
• You need to cite the source of every answer (compliance, legal, technical support)
• Your knowledge base is large (>10MB of text) but heterogeneous
• The budget is limited or the timeline is short
• Different users have access to different subsets of information (permissions)

When to use fine-tuning

Fine-tuning wins in specific cases where RAG can't compete:

• You need a very specific response style (brand tone, consistent structured format)
• Highly repetitive tasks with a rigid output format (classification, structured extraction)
• Very technical domains where the vocabulary requires deep understanding (medical diagnosis, specialized legal)
• Critical sub-200ms latency where retrieval adds unacceptable overhead
• Very high volumes where the per-query savings justify the initial cost

The hybrid: RAG + fine-tuning

In advanced business cases, the right answer is often both. Light fine-tuning handles style, format and specific instruction-following, and RAG handles updatable factual knowledge. This pattern is common in brand assistants with a strict style but a dynamic product catalog.

The most common mistake: choosing fine-tuning out of FOMO

Many executives ask for fine-tuning because it sounds more sophisticated and because "others are doing it". In practice, most of those projects are RAG cases in disguise. Symptoms of the wrong choice:

• The project has been running for 6+ months and is still collecting training data
• Every time a price or a product changes, you have to retrain
• The team doesn't know why the model gave a specific answer
• The project's cost exceeds 5x what an equivalent RAG would cost

If you recognize these symptoms, pause the project, run a 4-week RAG pilot and compare results. In 70% of cases the pilot wins.

Recommended stack by scenario

Basic RAG (companies with 10-200 people)

OpenAI/Anthropic + pgvector or Pinecone + LangChain/LlamaIndex. Typical cost: $8k-$25k MXN/month in operation after implementation.

Enterprise RAG (companies with >200 people)

Azure OpenAI or AWS Bedrock + Weaviate or Vespa + a custom pipeline with re-ranking and query rewriting. Observability with LangSmith or Arize. Typical cost: $40k-$150k MXN/month.

Fine-tuning

OpenAI fine-tuning API (GPT-4o-mini), Anthropic with Claude, or self-hosted with Llama/Mistral + LoRA. Requires 500-5,000 high-quality labeled examples.

Checklist before deciding

1. 1Does my information change more than once a quarter? → RAG
2. 2Do I need to cite the source of every answer? → RAG
3. 3Do I have >1,000 high-quality training examples? → Consider fine-tuning
4. 4Is critical latency <500ms? → Consider fine-tuning
5. 5Is my initial budget <$30k MXN? → RAG first
6. 6Can I sustain $50k+ MXN/month in operating costs? → Fine-tuning is viable