AI can write quickly. It cannot automatically know your customers, your standards, the decisions you already made, or the constraints you discovered after three weeks of research. When that information is missing, the model fills gaps with reasonable-sounding guesses. That is not a model failure. It is a context failure.

Context is the material someone would need before they could make a good decision about the project. It can include customer research, market observations, brand rules, technical constraints, existing architecture, source material, and decisions that are already settled.

The vague request problem

Imagine telling Codex:

It has to guess who the directory is for, how entries are chosen, what pages exist, whether users can save tools, whether there are accounts, and what not to build. You will get *a* directory. You may not get yours.

Now give it CONTEXT.md first:

# Product Context

## User
Independent founders who need practical AI tools for a real job today.

## What we learned
- Generic directories overwhelm people with too many weak listings.
- Users want to understand the use case and price quickly.
- A smaller curated collection is more useful than a giant catalogue.

## Product decisions
- Organize by job to be done.
- Start with curated entries.
- Let users save tools.

## Out of scope for version one
- User reviews
- Payments
- A native mobile app

The code may still need improvement. But the AI is now making decisions inside the world you actually want to create.

Context is not a dump

Giving an AI more text is not automatically better. A 200-page pile of notes can bury the important decisions. Good context is selected, organized, dated where necessary, and honest about uncertainty.

Use headings so the agent can find what matters. Put a date on time-sensitive research. Label an assumption as an assumption. If the answer is unknown, say it is unknown.

A practical context workflow

Chats, calls, sources, notesSelect what mattersCONTEXT.mdSPEC.md or prompt fileAI taskUpdate context when a decision changes

CONTEXT.md is not necessarily the only file. A mature project may keep separate customer research, brand guidance, architecture, and research-method files. The important habit is to put stable, reusable knowledge somewhere the next person—or agent—can find it.

Sonariq as a supporting example

In Sonariq, an agent researching a company should not begin with a blank page. It needs the shared research standards, the sector framework, the expected handoff format, and the specific job it is assigned. Those documents are context. They reduce the chance that ten agents produce ten incompatible versions of “research.”

Your project may be much smaller. The principle stays the same: give the AI the relevant operating reality before asking it to act.

Build your first context file

Create CONTEXT.md for your own project. Keep it to one or two pages at first.

# Context

## Problem
[What is happening now that should be better?]

## User
[Who has this problem?]

## Evidence
- [Useful observation or source]
- [Useful observation or source]

## Decisions already made
- [Decision and why]

## Constraints
- [Budget, timeline, technology, brand, privacy, or quality constraint]

## Open questions
- [What still needs a real answer?]

Ask an AI: “Read CONTEXT.md. List the facts, assumptions, decisions, and open questions separately. Do not invent answers to the open questions.” That is a useful test. If the AI cannot separate them, your context is not clear enough yet.

Check your understanding

1. What is the difference between context and a prompt? 2. Why can too much unstructured context be harmful? 3. What should happen when a material project decision changes? 4. What would an AI need to know before it could make a good decision about your project?