Context Engineering Cheat Sheet: What to Give Your AI

A context engineering cheat sheet is a short checklist of what an AI assistant needs to do a particular task: the goal, trusted sources, rules, current state and checks before acting. Use it to select useful information, then verify that the assistant actually received and understood it.

The checklist stays short when the underlying work record is maintained. fenbs can hold your current project rules and progress, so each new conversation starts from saved work rather than your memory of the last chat.

What belongs on the one-page checklist?

Start with the outcome and work backward. An assistant preparing a proposal needs the approved scope and audience; one checking a spreadsheet needs the definitions behind its columns. Both need a clear stopping point and a way to recognize missing information.

Keep examples concrete. Replace “follow our usual style” with the name of an approved example and the two features that matter. Replace “finish the report” with the sections needed and the checks expected. A checklist works when its blanks expose uncertainty before that uncertainty becomes a confident answer.

What template can you copy into your next task?

Copy the following structure and fill it with facts for one task. The labels organize your preparation; they are not a special format that every model requires. Leave an unknown explicitly unknown instead of inventing a value to complete the form.

GOAL
Produce: [specific result]
For: [reader or user]
Finished means: [observable checks]

SOURCES
Use: [document or record, section, version]
Authority: [who owns or approved it]
Missing access: [what cannot be read]

RULES
Must: [requirements]
Must ask before: [approval boundary]
Exclude: [out-of-scope work]

CURRENT STATE
Done and checked: [work and evidence]
Still open: [uncertainties]
Start here: [next action]

RETRIEVAL CHECK
Before working, identify the source supporting the key rule.
Explain any conflicting or missing information.
After working, save what changed, what was checked and what remains.

For a hypothetical design studio, “produce” could mean a revised homepage outline for a local restaurant. The source is the approved menu and client meeting notes. The rule is to preserve the reservation link; the current state says the client accepted the layout but has not approved the new headline.

That distinction changes the work. The assistant may propose headlines, but it should not describe one as approved. The useful context is the status of the choice, not a transcript of every headline discussed. Keep reusable wording in fenbs Skills and prompts, and fill it from the current project record when you use it.

How much information should you include?

Include enough to make the task unambiguous, then add detail when a real gap appears. Anthropic’s context engineering guidance↗ (opens in a new tab) favors relevant information selected for the outcome. That supports using this sheet to make choices, rather than attaching your entire archive.

Give the assistant the policy paragraph that governs the proposed change, with a route to its surrounding document. Include exceptions when they alter the answer. A short excerpt without its qualifying sentence can be less useful than a longer passage with the necessary conditions intact.

• Keep: the active goal, applicable rules, source references and verified current state.
• Retrieve when needed: background documents, earlier examples and supporting detail.
• Remove from the immediate task: unrelated conversations, abandoned alternatives and repeated copies.
• Flag clearly: disputed facts, unapproved proposals and evidence the assistant cannot access.

The broader distinction between wording a request and managing its supporting information is covered in context engineering vs prompt engineering. Here, the practical test is whether each piece of information changes the next action or its verification.

How do you check that the right context arrived?

Ask for a short read-back before consequential work: the intended output, the rule that matters, the supporting source and anything still unknown. Check the answer against the actual record. A correct-sounding summary is insufficient if its cited source describes an older decision.

GitHub’s prompting guidance↗ (opens in a new tab) recommends relevant context, clear requirements and review of generated output. Apply that advice by making the assistant identify the specific material it used, then checking its work against the same requirements.

Use three quick trials: an ordinary task, a task with an exception, and a question the sources cannot answer. The third should produce an explicit gap or a question for you. If the assistant fills that gap from assumptions, strengthen the stopping rule and repeat the trial.

Application setup affects what is available to read. Use context engineering in Cursor for that editor’s workflow or context engineering for Claude Code for its coding workflow; the checklist remains a check on the task itself.

How to do this with fenbs

1. Create a free account and add one active project to your personal kanban board.
2. Add the current task and its goal. Put approved ongoing instructions in Decisions and rules, and save the latest verified position in Where we left off.
3. Save a reusable version of the checklist in Skills and prompts. Keep project facts in their own records so the template does not become a second, stale copy.
4. Connect your supported AI app through Integrations, then ask it to read the project context and complete the checklist before working.
5. Review the result and ask the assistant to save progress, including checks performed, remaining uncertainty and where the next session should begin.

fenbs is free for one person, no credit card. It is useful when project facts must survive across conversations; a single short document may be enough for a one-time question. fenbs does not automatically read your chat history or turn your document archive into a search system.

When should you refresh the checklist?

Refresh it when the goal, source, rule or work state changes. If a person approves a different approach, record that decision before the next session. If a check fails, replace an optimistic “done” summary with the actual result and next step.

Keep the empty template separate from its completed copies. The template reminds you which questions to answer; the project record supplies those answers. If you paste a client-specific exception into the reusable template, an unrelated future task may inherit it. A useful final check is to ask whether each statement belongs to this task, this project or all of your work.

Review your saved version from the next reader’s position. Can someone find the source without asking you, distinguish an assumption from a decision and recognize the next action? Correct those gaps while the details are fresh. That maintenance makes the next checklist quicker to complete.

A reusable context engineering skill is a separate way to package a repeatable process; this sheet is the compact reminder you can use today. Browse the context engineering guides when you need a deeper answer about one part of the workflow.