Context Theory Get your growth audit

Answer

How do you get an AI to remember what it needs to know about a project?

Write it down in a file the system reads at the start of every session. Conversation memory is not the mechanism.

Keep a short written file of decisions and constraints that the system reads at the start of every session. Conversational memory is unreliable and invisible; a file is neither. What goes in it should be decisions, not descriptions.

The question usually arrives after an assistant has forgotten something it was told two days earlier, and the instinct is to look for a memory setting. That is the wrong layer. Whatever retention a given product offers, it is opaque, it varies, and it is not inspectable — you cannot open it, read what it thinks it knows, or correct an entry that is wrong. Anything load-bearing needs to live somewhere you can read.

The mechanism that works everywhere is a plain file that gets supplied at the start of a session: project instructions, a brief, a context document, whatever the tool calls it. Its job is not to describe the project. It is to record the things that would otherwise be re-explained, re-decided or re-litigated. Decisions that have been made and are closed. Constraints that are not negotiable. Names and identifiers that are easy to get wrong. Conventions the work must follow.

What does not belong is anything the system can find out for itself, and this is where these documents go bad. A file listing what every folder contains is duplicating something inspectable, and it will drift out of date silently while continuing to be believed. The same applies to status, progress and anything with a date attached. The test is whether the fact is discoverable: if the system could establish it by looking, leave it out, and if it could not, write it down.

The second failure is accumulation. These files grow because adding a line is the obvious response to any mistake, and nothing ever gets removed. A long instruction file dilutes itself — every instruction competes for attention with every other one — and past a certain length the system follows the document approximately rather than exactly. Pruning is maintenance, not tidying, and the practical rule is that an instruction which has not been needed for a month is a candidate for deletion.

The third is that written decisions rot. A constraint recorded in March may have been reversed in June by a conversation nobody wrote down, and the file will keep asserting it with total confidence. Dating entries helps. Recording why a decision was made helps more, because a reader can then tell whether the reason still holds, which is not something the decision alone can tell them.

For work that spans several sessions there is a second document doing a different job: what is currently in progress and what the next step is. Keeping that separate from the durable decisions is worth the small overhead, because the two have opposite lifespans. Mixing them produces a file whose stale half discredits the reliable half, and readers cannot tell which half they are looking at.

Project memory is a document you maintain, not a feature you enable, and the difference shows up the first time an assistant confidently contradicts last week.

Siddharth Sharma, Context Theory

Related questions

Should the file be written by the assistant or by a person?

Drafted by either, decided by a person. The specific hazard in letting a system maintain its own instructions unsupervised is that it will record its interpretation of a correction rather than the correction, and the interpretation is what gets applied forever afterwards. Reading the diff before it lands takes a minute and is the whole control.

How long should it be?

Short enough that you can read it in full when something goes wrong, because that is when you will need to. If a page has grown past the point where anyone checks it against reality, its errors have become permanent. Splitting by area helps more than compressing, since a reader can then verify one section at a time.

METHOD

Every figure below carries its source and the date it was verified. Nothing on this page is asserted.

The numbers on this page.

Datapoints
What Value Specific to
Visibility lift in AI-generated answers from GEO methodsup to 40%Category-wide
AI-cited sources that also rank in the Google organic top 1010%Category-wide

Aggarwal et al., "GEO: Generative Engine Optimization", Princeton / Georgia Tech / IIT Delhi / Allen Institute for AI — KDD 2024 · GEO-bench · 10,000 queries across 8 domains · verified

2026 generative engine citation study · fewer than · verified

What is specific to this page.

Evidence
Kind Claim Check it against
SoftwareProduct-level conversational memory is not inspectable or correctable by the user, so anything a project depends on has to live in a file that can be opened and edited rather than in retained conversation state.Attempting to view and edit the retained memory of any assistant product that offers one, and comparing that with editing a project instructions file.
WorkflowThe admission test for a project instructions file is discoverability: a fact the system could establish by inspection duplicates something already true and will drift silently, while a decision or constraint that leaves no trace in the artefacts is the only class that must be written down.Checking each line of an existing instructions file against whether the system could determine it from the project itself.
ResponseAn instruction file dilutes itself as it grows because every instruction competes with the others for attention, so past a certain length the document is followed approximately rather than exactly and pruning becomes maintenance rather than tidying.Testing adherence to a specific instruction placed in a short file and in the same file after unrelated additions.
ConstraintDurable decisions and current progress have opposite lifespans, so keeping them in one document lets the stale half discredit the reliable half with no way for a reader to tell which is which.Reading any long-lived project document and marking which statements are still true, then checking whether the stale ones are visually distinguishable.

Each row would be wrong on another industry's page. Where a sourced figure exists it is in the table above instead; these are the constraints that shape the work and do not happen to be numbers.

Start with the measurement.

Reading about a benchmark is not the same as knowing your own number. The audit produces yours, measured rather than estimated.

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