What is Fine-Tuning? Fine-Tune vs Prompt

What is Fine-Tuning?

Fine-tuning is the process of taking a pre-trained large language model (LLM) and continuing to train it on a curated dataset — adjusting its weights so the model behaves better for a specific task, domain, or style.

Unlike prompting (where you guide the model at inference time), fine-tuning modifies the model itself. A fine-tuned model has genuinely different behavior baked in — it doesn't need a long system prompt to know how to act, because that knowledge is encoded in its parameters.

Fine-tuning is powerful. It's also expensive, slow, and often unnecessary — which is why most teams reach for it too early and make it one of the biggest AI MVP mistakes.

How Fine-Tuning Works

Modern LLM fine-tuning uses supervised fine-tuning (SFT) as the starting point:

Collect a dataset — Input/output pairs that demonstrate the behavior you want. Usually hundreds to tens of thousands of examples.
Format training data — Structure inputs as {"prompt": "...", "completion": "..."} or in chat message format.
Run training — Update the model's weights on your dataset using a training framework (OpenAI fine-tuning API, Hugging Face transformers, Axolotl, Unsloth).
Evaluate — Compare fine-tuned model against baseline on your eval set.
Deploy — Host the fine-tuned model (OpenAI API, vLLM, Together AI, etc.).

For large models, parameter-efficient fine-tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are standard — you update a small adapter on top of the frozen base model rather than all weights. This reduces compute by 10–50× with minimal quality loss.

Fine-Tuning vs Prompting vs RAG

This is the most important decision in AI product design. Most teams default to fine-tuning when they should be prompting or using RAG.

| | Prompting | RAG | Fine-Tuning | |--|-----------|-----|-------------| | Cost | Lowest | Low | High | | Speed to ship | Hours | Days | Weeks–months | | Data freshness | Always current | Real-time | Stale after training | | Best for | Format/tone, one-off tasks | Domain knowledge, Q&A over docs | New skills, persistent style/behavior | | Data required | None | Unstructured docs | 100–10K+ labeled examples | | Hallucination risk | Moderate | Low (grounded in retrieval) | Moderate |

Decision tree:

Need the model to know something? → RAG first
Need the model to behave differently? → Prompting first, then fine-tuning if prompting fails
Need low latency without a large system prompt? → Fine-tuning
Need to teach a fundamentally new skill the base model doesn't have? → Fine-tuning

When Fine-Tuning Is the Right Call

Fine-tuning earns its cost and complexity when:

1. Style and tone are core to the product If your product needs a very specific voice — technical writing for a niche domain, legal document drafting in a particular jurisdiction's style — prompts alone often drift. A fine-tuned model holds tone consistently across thousands of outputs.

2. You need short prompts at high volume System prompts add tokens, which add cost. At millions of API calls per month, a fine-tuned model that doesn't need a 2,000-token system prompt can meaningfully reduce your bill.

3. You have a well-defined structured output task Classification, entity extraction, translation for a specific domain — these are tasks where fine-tuning on labeled examples reliably outperforms prompting.

4. You're building on a small/open-source model If you need to run on-premise or use a 7B parameter open-source model (Llama 3, Mistral, Gemma), fine-tuning is often what makes it viable for production. Small models can't follow complex instructions reliably without it.

When Fine-Tuning Is the Wrong Call

Fine-tuning is often proposed too early. Avoid it when:

Your data changes frequently — A fine-tuned model is frozen at training time. If your knowledge base updates weekly, use RAG.
You have less than a few hundred high-quality examples — Bad training data produces a model that confidently does the wrong thing. Quality beats quantity, but you need both.
You haven't tried prompting first — Most tasks that "need fine-tuning" can be solved with a well-crafted system prompt and few-shot examples. Try that first. It's free.
You're pre-revenue — Fine-tuning is a week-four or week-eight problem for most startups. Validate the product first.

Fine-Tuning with OpenAI

OpenAI's fine-tuning API supports GPT-4o mini and GPT-3.5 Turbo:

{"messages": [{"role": "system", "content": "You are a legal document assistant."}, {"role": "user", "content": "Summarize this clause in plain English."}, {"role": "assistant", "content": "This clause means the seller..."}]}

Upload a JSONL file with at least 10 examples (50+ recommended), kick off training via API or dashboard, and your fine-tuned model is available as a custom endpoint within hours.

Cost: $8 per million training tokens for GPT-4o mini. A 1,000-example dataset of 500 tokens each costs ~$4 to train.

Fine-Tuning with Open-Source Models

For teams with privacy requirements or cost constraints, open-source fine-tuning is increasingly practical:

Llama 3 (8B or 70B) — Meta's open-weights model, strong baseline
Mistral 7B — Efficient, well-documented fine-tuning community
Framework: Hugging Face trl + LoRA adapters via peft
Compute: A single A100 GPU for a few hours handles most LoRA fine-tuning jobs (available on Lambda Labs, Vast.ai, or Google Colab for small experiments)

Evaluating Your Fine-Tuned Model

A fine-tuned model is only better if it actually improves on your eval set. Measure:

Task accuracy — Does the model produce correct outputs on your held-out test set?
Regression — Did it get better at the fine-tuned task while getting worse at general reasoning?
Format consistency — Does it reliably follow output format requirements?

Always compare fine-tuned model vs. best-prompt baseline on the same eval set before making the call.

Key Takeaway

Fine-tuning is a precision instrument, not a hammer. Use prompting for most tasks. Use RAG for domain knowledge. Fine-tune when you need persistent behavioral change, volume efficiency, or a small model to punch above its weight.

Trying to decide between prompting, RAG, and fine-tuning for your product? We help founders make this call every week. Book a 15-min call →

What is Fine-Tuning? Fine-Tune vs Prompt