---
title: Fine-tuning versus context — AI basics, lesson 8
description: Training teaches a model how to answer; a page in its context teaches what is true today, with a source. Nine methods, deepest first, and where a fact belongs.
url: https://instibrain.ai/guides/ai-basics/fine-tuning-versus-context
updated: 2026-11-08
site: https://instibrain.ai/llms.txt
---

lesson 8 of 8 · ai basics

# Fine-tuning versus context.

Training teaches a model how to answer. A page in its context teaches it what is true today, with a source. Facts belong on pages.

## new words

**Fine-tuning**
Any further training of a finished model on your examples. It changes the weights, and teaches format and tone more than facts.

**Pretraining**
Training from nothing on a huge body of text, to predict the next token. It fixes the knowledge cutoff.

**Knowledge cutoff**
The date after which the model saw nothing. A newer fact needs retrieval or context.

**Instruction file**
A file an AI reads at the start of a session, such as CLAUDE.md. The simplest durable context.

## the lesson

Nine methods change what a model knows or how it acts. Six change the weights, two fetch at question time, one tells it at the start.

| Method | Lever | What it does |
| Pretraining | weights | Trains from nothing on trillions of tokens, How a model works, lesson 7. Everything, frozen at the cutoff date. |
| Continued pretraining | weights | More pretraining on one field's text. Vocabulary and style; facts without a source. |
| Supervised fine-tuning | weights | Example answers teach format, tone and the shape of a task. Not facts. |
| Preference tuning | weights | Better-versus-worse pairs teach judgment. |
| LoRA, adapters | weights | Trains a small add-on while the base model stays frozen. Fine-tuning at a fraction of the cost. |
| Distillation | weights | A big model writes answers; a small one learns to copy them. |
| RAG | retrieval | Chunk, embed, index. Fetches the nearest chunks into the prompt, cited. Lesson 7. |
| Tools, MCP | retrieval | The model looks things up and acts, live, through structured calls. |
| Context | context | Instruction files, pages and skills, read at the start of every session. |

Deeper is costlier, slower to redo, and staler. A fine-tuned model knows nothing written after its training run, cannot say where a fact came from, and carries your data inside a vendor's weights. A page is fresh on save, is its own source, and stays in your space.

So a fact goes on a page. A way of answering goes in an instruction file first, and into a fine-tune only when a file cannot carry it. Judgment comes from a better model. Your Claude reads your pages before it works; nothing about your company is trained into it, and the same model answers another company from its own pages.

The same five questions put to both ways of making a model yours.

## try it

Type this and press Enter:

```
Read the CLAUDE.md you loaded at the start and tell me one rule from it. Did that rule change anything inside the model, or only what you were told?
```

## you should see

- One rule, quoted from the file.
- Only what it was told: the rule sits in its context for this session, the model's weights are the same for everyone, and a change to the file changes the rule at the next start.

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