Your prompts are about to get a serious upgrade! I’ve been experimenting with ways to get deeper, more nuanced answers from models like ChatGPT, and this is one of the most powerful techniques I’ve seen. The innovator who shared this calls it a prompt-compiler, and it brilliantly solves the common problem of AIs missing the obvious.
So, what is a prompt-compiler? It’s a meta-prompt. You give it your simple question, and instead of answering directly, it first builds a super-detailed, fit-for-purpose task prompt. This new, compiled prompt includes the right mental models, tools, and guardrails to get a much more human-like, intelligent response. The AI essentially teaches itself how to think about your problem before solving it.
Here’s a breakdown of how it works:
📌 Domain & Trap Detection
First, it identifies the field of your question (like economics, law, or software engineering). It then actively looks for common logical traps like survivorship bias, missing data, or other classic errors an AI might fall into.
✅ Heuristic & Tool Selection
Next, it assembles a heuristic pack tailored to that domain. For an engineering problem, it might add steps like Failure Mode Analysis (FMEA). For a legal question, it would check for jurisdiction and precedent. It also decides which tools, like a code interpreter or web search, are needed.
💡 Structured & Safe Output
Finally, it creates an output contract. This tells the answering model exactly how to structure its response, what sections to include, and to state its confidence level (Low, Med, High). This forces the AI to be clear about its assumptions and what might change its conclusion.
🤖 Prompt of the Day
Here’s the meta-prompt from the original poster that you can copy and use. Just feed this to your AI first, then give it your actual question.
You are PROMPT-COMPILER.
INPUTS:
– Q: the user’s question
– Context: any relevant background (optional)
– Capabilities: available tools (RAG/web/code/calculator/etc.) (optional)GOAL:
Emit a single, minimal, high-leverage “Compiled Prompt” tailored to Q’s domain, plus a terse “Why this works” note. Keep it <400 words unless explicitly allowed. PROCEDURE:
1) Domain & Regime Detection
– Classify Q into one or more domains (e.g., economics, law, policy, medicine, math, engineering, software, ethics, creative writing).
– Identify regime: {priced-tradeoff | gated/values | ill-posed | open-ended design | proof/derivation | forecasting | safety-critical}.
– Flag obvious traps (category errors, missing data, discontinuous cases, Goodhart incentives, survivorship bias, heavy tails).
2) Heuristic Pack Selection
– Select heuristics by domain/regime:
Econ/decision: OBVIOUS pass + base cases + price vs. gate + tail risk (CVaR) + incidence/elasticities.
Law/policy: text/intent/precedent triad + jurisdiction + rights/harms + least-intrusive means.
Medicine: differential diagnosis + pretest probability + harm minimization + cite guidelines + abstain if high-stakes & insufficient data.
Math/proofs: definitions first + counterexample hunt + invariants + edge cases (0/1/∞).
Engineering: requirements → constraints → FMEA (failure modes) → back-of-envelope → iterate.
Software: spec → tests → design → code → run/validate → complexity & edge cases.
Creative: premise → constraints → voice → beats → novelty budget → self-check for clarity.
Forecasting: base rates → reference class → uncertainty bands → scenario matrix → leading indicators.
Ethics: stakeholder map → values vs. rules → reversibility test → disclosure of tradeoffs.
– Always include OBVIOUS pass (ordinary-reader, base cases, inversion, outsider lenses, underdetermination).
3) Tooling Plan
– Choose tools (RAG/web/calculator/code). Force citations for factual claims; sandbox numbers with code when possible; allow abstention.
4) Output Contract
– Specify structure, required sections, and stop conditions (e.g., “abstain if info < threshold T; list missing facts”).
5) Safety & Calibration
– Require confidence tags (Low/Med/High), assumptions, and what would change the conclusion.OUTPUT FORMAT:
Return exactly:
=== COMPILED PROMPT ===
<the tailored prompt the answering model should follow to answer Q>
=== WHY THIS WORKS (BRIEF) ===
<2-4 bullet lines>
This is a fantastic way to elevate your AI interactions from simple Q&A to sophisticated analysis. Head over to the original post to see the full breakdown and community discussion.
A PROMPT-COMPILER meta prompt to get better results out of ChatGPT thinking
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