Becoming an AI Power User

1.10The Expanding Edge

They save what they learn from each AI session where the next session can use it

Developing

Save the AI fixes you would otherwise make again

Suppose you read long reports for work and ask an assistant to summarize them. The first summaries open with two paragraphs of background, and one states a projected figure as if it had already happened. After a few rounds of correction you land on an instruction that works: "Lead with the author's main finding, then the numbers that support it, and flag anything the author calls uncertain." Next week a new report arrives and you open a new conversation. That sentence is somewhere in last week's chat, so you retype it from memory, and this time you leave out the part about uncertainty.

A small library gives a lesson like that one a home where you and the assistant can find it again. Each entry holds the instruction, what it is for, and where it applies, so the next session starts from what you already worked out.

The saved entry also holds the details memory drops. You may remember that the summaries got better and forget which wording did it. The entry keeps the exact sentence, so when you want to change it, you and the assistant are working from the same text.

Three kinds of entry make a good start: standards, task context, and examples. Begin with what you expect to reach for again, and add a kind when a need keeps coming up.

Standards: rules any future output for this task has to meet. 'Lead with the author's main finding' is a standard for every report summary you ask for.
Task context: what one task or project needs to know up front, such as who the output is for, what you are looking for, and what to skip. 'I am reading these for next month's vendor decision, so pull out anything about pricing and contract length' is task context.
Examples: outputs to imitate, each kept with a line on why it worked. You point the assistant at a summary that handled a hedged forecast well and ask it to do the same with the next report.

A minimum viable asset library is the smallest version that works: one folder or one note holding a handful of entries. Keeping it small keeps the right entry easy to find and hand to the next session. The finding-first standard sits there for every summary you will ever ask for. The vendor note sits in a separate project note and leaves when the decision is made.

Save each AI lesson as a short instruction instead of a whole transcript

A short instruction is easier to reuse than a long transcript. The report standard is one sentence, and the conversation that produced it ran for pages. Write the lesson in plain words and link the original exchange when you may want its evidence later, such as the summary that reported the forecast as fact. Keep the transcript as a record if it helps, and hand the next session the instruction.

Keep one project's facts apart from reusable AI standards

The finding-first standard helps with every report you read. The vendor note helps with one decision and goes stale once that decision is made. A client's private details, contract terms, and a temporary deadline belong with their project too, where they are much less likely to turn up in unrelated work. Label each entry with what it is for and where it applies, which tells the assistant to load the standard for every summary and the vendor note only for the vendor work. An entry travels well when it carries nothing that belongs to a single project.

Keep entries in plain text any assistant can read

Store the library somewhere that opens, searches, and exports without depending on one product. A folder of text files, a notes app, or an assistant's project all work, provided an entry copies out cleanly and pastes into a different assistant. Assistants change often, and plain text moves with you. When you switch assistants or rebuild your setup, hand the new one the standard, the current project note, and the example summary, then compare its first summary with the saved one.

Keep what is likely to come up again. A correction you have typed three times deserves a standing instruction. A costly one-time mistake, like the forecast reported as fact, can deserve a record even if that kind of report never comes back. Saving every passing reaction as a rule fills the library with rules that fit nowhere and buries the ones that help.

Ask the assistant which parts of an AI session to save

The assistant that produced a result can reread the session and propose entries for you. After a run goes well, and while the conversation is still open, ask it for candidate standards, task context, and examples, each with the exchange that supports it and the tasks it covers. In the report case, it might propose the finding-first standard, the vendor note, and the summary that handled the forecast. Read the proposals before you save them, because an assistant can mistake a one-time correction for a lasting preference.

A result you admire can teach as much as a correction. Suppose a colleague sends you a report summary you would happily read every week. means asking the assistant to study that result and propose instructions that could produce more like it. Treat what comes back as a guess to test on your next report, since many different prompts could have produced the same summary.

Save the reason alongside each AI instruction

A full library entry records the instruction, its reason, and where it applies

Name: Report summaries: main finding first, uncertainty flagged. Instruction: Open with the author's main finding in one sentence. Follow with the numbers that support it and where each appears in the report. List anything the author calls uncertain, preliminary, or estimated on its own line. Reason: I read these summaries to decide whether a report deserves a full read, and an early summary presented a projected figure as a measured one.

Applies to: Summaries of reports and long articles I read for work. Meeting notes and email threads need their own entry. Refresh each run: The report itself and what I am reading it for this time. Kept with it: A summary that handled a hedged forecast correctly, and the early summary that stated the forecast as fact, marked as the mistake this entry prevents.

The reason line lets a later assistant apply the entry sensibly. Say next month you hand it a report whose author flags nothing as uncertain. An assistant that knows why the uncertainty line exists can write "The author marks no figures as uncertain" and move on, where one following the bare rule might invent a caveat to fill the slot. The reason also lets you change the format later, say to a shorter summary for your team, and keep the finding-first order without copying last month's wording.

A library helps only when the entry you need is easy to find. Twenty unlabeled drafts in one folder bury the one instruction that works. Give each entry a clear name and scope, and move old versions out of the way when you replace them. Keep full records where you need them, with a short instruction up front that points to them.

Each time you use an entry, check that it still fits. If you start reading reports to brief your team instead of to decide what to read yourself, the reason line changes, and the instruction may need to change with it. Small edits like that keep the library matched to the work you do now.

Find what in an AI session to save

Claude reviews one recent AI session and proposes standards, task context, and examples to keep, written so a future session can use them. You approve what goes into your library.

Copy the promptUse the copy button above the box.
Paste it in Claude or CodexMake sure your active project is enabled.
Answer the questionsGive clear answers so it understands what you want made.
Test it to see if it worksTry the output and refine until it does what you need.

Problem: Useful corrections and examples can stay buried in a finished AI conversation. Intent: I want to find what a similar future task could reuse and review it before saving it in my library. Use the relevant session if it is available here. Otherwise, ask me for the conversation or a summary of it, and the work it served. If details are missing, ask what improved, which corrections I gave, and what supports my assessment. If I supply only a summary, attribute findings to that summary and mark details that need the original exchange. 1. Separate the finished output from the evidence of how it improved: my corrections, the constraints that mattered, and choices the session supports. 2. Propose reusable entries where the evidence supports them: - STANDARDS: rules future outputs for this task should meet. - TASK CONTEXT: what a similar task needs to know up front. - EXAMPLES: outputs to imitate, with a line on what makes each one work. An entry in every category is optional. 3. Write each entry so a future session can use it without the full conversation. Cite the supporting passage or my account, give the reason for the entry, and state its scope: a general preference, a rule for this kind of task, or facts for this project only. Do not invent personal facts, preferences, or supporting quotations. 4. Flag temporary facts, exceptions, uncertainty, and any inference that still needs testing. Say which entries need a date or a link to the original exchange. A correction for one particular case can stay a one-off. 5. If you do not know where I keep reusable notes or instructions, ask. Suggest a place that fits my answer and that I can search and export, and explain how the next session would load the entries it needs. Show the proposed entries and where each would go. Ask me to confirm the wording and scope of any personal facts and preferences. Save nothing I have not approved.