# Fine-tuning

> Part of the AI Garage Jargons glossary: https://aigarage.in/jargons (all terms: https://aigarage.in/jargons.md)

**Category:** Machine Learning
**Tags:** customization, model-training, evaluation, parameters

Additional training that uses representative examples or feedback to update a pretrained model's parameters for a more specific task or behavior.

**In plain terms:** It changes an existing model by training it on examples of how you want a particular task performed.

**Use cases:**
- Consistent structured outputs
- Task-specific classification
- Specialized response behavior

**Why it matters:** Fine-tuning can make a repeated behavior more consistent or reduce the instructions needed at inference time. It also creates a new model version that must be evaluated, deployed, and maintained like any other trained artifact.

**Why now:** Current model platforms offer supervised and preference-based tuning for specialized behavior. Their guidance still starts with a prompt baseline, representative evaluation data, and high-quality training examples before adding the cost and operational work of tuning.

**AI Garage example:** After collecting and reviewing many session-to-workbook examples, AI Garage could fine-tune a model to produce a consistent workbook structure. The current session evidence would still be supplied at inference time rather than treated as reliable factual memory inside the tuned model.

**Mental model:** Prompting changes what a model sees for one request. Retrieval adds temporary evidence. Fine-tuning changes learned parameters through a separate training run, producing a model version whose behavior must be tested before it replaces the previous version.

**Builder scenario: Improve a repeated extraction task without baking in current facts**

- **Context:** A support workflow repeatedly converts varied customer messages into the same reviewed JSON schema, but a prompt with several examples is long and still produces recurring format errors.
- **Failure:** The team fine-tunes on a small, inconsistent dataset and assumes the new model will also remember every future policy change, so format regressions remain and answers become stale.
- **Intervention:** Create representative training and holdout sets from reviewed examples, fine-tune for the extraction behavior, evaluate the new model against the prompt baseline, and continue supplying current policy evidence separately.
- **Outcome:** The evaluated tuned model returns the required schema more consistently with shorter instructions, while policy updates remain available immediately through the runtime evidence path.

**Common misconceptions:**
- **Claim:** Fine-tuning is the best way to upload documents so a model always knows their latest facts.
  **Correction:** Fine-tuning changes model behavior through training. Current or frequently changing facts are usually better supplied at request time through prompts or retrieval, where the source can be updated and inspected.

**Visual model (system):** Fine-tuning changes a model version through training. Prompting and retrieval remain separate runtime paths for instructions and current evidence.

**Visual description:** A base model and reviewed examples enter a separate parameter-update process that produces a tuned model version, which is tested on a holdout evaluation before deployment.

**Related concepts:** [Prompt Engineering](https://aigarage.in/jargons/prompt-engineering), [RAG (Retrieval-Augmented Generation)](https://aigarage.in/jargons/rag), [Inference](https://aigarage.in/jargons/inference), [Large Language Model](https://aigarage.in/jargons/llm)

**Video explainer:** [Fine-tuning: change behavior through training](https://nqqbubjesbyuwrdhqxnk.supabase.co/storage/v1/object/public/aigarage-public-media/jargons/fine-tuning/v2/fine-tuning.mp4) (52 seconds)

See how reviewed examples update model parameters, how holdout evaluation protects deployment, and why current facts still belong in the runtime evidence path.

**Published:** 2026-10-11T21:02:07+05:30
**Captions:** [English](https://aigarage.in/jargons/fine-tuning/captions.vtt)
**Narration:** generated

**Transcript:** A support prompt fills in the right fields most of the time. Sometimes it misses.

Fine-tuning is extra training that teaches an existing model your exact task.

First, the team gathers reviewed examples of correct results.

They keep some back, called a holdout, to test with later.

Training then adjusts the model's internal settings, called parameters, and produces a new version.

That version is not trusted yet. It runs on the held-back examples, next to the old prompt.

If it does worse, the team blocks it.

If it does better, the fields come out more consistently, with shorter instructions.

But current policies still arrive with each request, through the prompt or a search step.

Fine-tuning changes how a model behaves. It is not where changing facts should live.

**Last researched:** 2026-10-11

**Sources:**
- [Google Cloud - Introduction to tuning](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/tune-models)
- [Google for Developers - Fine-tuning, distillation, and prompt engineering](https://developers.google.com/machine-learning/crash-course/llm/tuning)
- [OpenAI API - Optimizing LLM accuracy](https://developers.openai.com/api/docs/guides/optimizing-llm-accuracy)

**URL:** https://aigarage.in/jargons/fine-tuning