What the AI Can and Can't See

Start here, because it explains most of the rest. A language model does not remember your last conversation. It does not remember your project between sessions. Every single time you press enter, the tool assembles a package of text and sends the whole thing — and that package is the entire world as far as the model is concerned.

That package usually contains:

1. 1
   A system prompt

   Instructions from the tool vendor you never see.

2. 2
   Your rules file

   AGENTS.md, CLAUDE.md, or .cursorrules, if one exists.

3. 3
   Some of your code

   The open file, files you referenced, and whatever the tool's search decided was relevant.

4. 4
   The conversation so far

   Earlier messages in this session, until they get too long and start being dropped.

5. 5
   Your actual prompt

   The smallest part, and the only one you fully control.

When the model “forgets” something you said twenty minutes ago, nothing mystical happened. That text stopped being included in the package.

There is a hard ceiling on how much text can go in that package, measured in tokens — roughly ¾ of a word each, so 1,000 tokens is about 750 words. Modern models take a lot: hundreds of thousands of tokens. That sounds limitless until you notice a mid-sized codebase is millions.

So the tool is constantly choosing what to include and what to leave out. It is usually good at this. It is never perfect. And critically: it will not tell you what it left out.

This is the behaviour people find most alarming, and it has a mundane explanation. The model predicts plausible text. When it cannot see your actual formatDatehelper, it does not think “I lack information.” It thinks “what would a formatDate helper look like in a project like this?” — and writes that.

The output is confident because plausible text is confident text. There is no internal signal that distinguishes “I read this” from “I inferred this.”

Rather than guessing, ask. This costs one message and tells you more about your setup than any amount of theory:

Before answering anything else: list the files you can currentlysee in this project, and tell me which parts of my codebase youcannot see. Do not guess — if you are unsure, say so.

Run this in each tool you use. A repo-aware agent lists real files. An autocomplete extension names your open file and little else. A browser chat says it sees nothing but what you pasted. All three answers are correct, and knowing which one you are holding changes how you prompt.

1. 1

   Reference files explicitly

   Do not make the tool guess which of your 200 files matters. Naming two files is faster than any search it can run.

2. 2

   Start fresh when the thread wanders

   A new conversation with a good opening prompt beats a long one that has drifted. You are not losing progress — the code is on disk.

3. 3

   Put durable facts in a rules file

   Anything you find yourself repeating every session belongs somewhere the tool loads automatically.

   That is Chapter 15, and it is the highest-payoff thing in this part of the course.

4. 4

   Paste errors in full

   Your summary of an error drops the stack frame that identified the file. The raw text costs you nothing.