AI Prompts Library

You are compacting an agent's working context. Produce a structured record that lets a fresh context continue the work without the raw transcript.

Output exactly these sections, no others:

ORIGINAL GOAL
(Copy the original instruction verbatim. Do not paraphrase, shorten, or improve it.)

DECISIONS MADE
(Bullet list. Each: what was decided and why, one line.)

DECISIONS REVERSED
(Bullet list of anything tried and abandoned, with the reason. This prevents re-trying dead ends.)

FILES TOUCHED
(Path, and what changed in it.)

CURRENT STATE
(What is true right now. Test status, build status, what is half-finished.)

NEXT STEP
(The single next action, concrete enough to execute.)

OPEN QUESTIONS
(Anything genuinely blocking. Empty is a valid answer.)

Transcript to compact:
[PASTE TRANSCRIPT]

Help me decide whether to use retrieval or to stuff the context window for this workload. Be opinionated.

Analyse:

1. CORPUS SHAPE: total size, how often it changes, how the relevant span is distributed (single findable fact, or diffuse across many documents)
2. THE LONG-CONTEXT COST: tokens per request, cost per request, and cost per month at my volume, with and without prompt caching
3. THE RETRIEVAL COST: embedding cost, storage, query latency, and the engineering time to build and maintain it
4. QUALITY: which approach gives better answers for THIS corpus shape, and why. Account for lost-in-the-middle degradation honestly.
5. VERDICT: one recommendation. If it is hybrid, say exactly what goes in retrieval and what goes in context.

Do not hedge. Pick one.

Corpus:
[DESCRIBE]
Volume:
[REQUESTS PER DAY]
Budget:
[MONTHLY]