GPT-5.6 Luna · prompting-guide
Turn “GPT-5.6 Prompting Guide: Luna's Work Contract and Model Routing” into a bounded task entry with explicit inputs, runtime context, output format, and acceptance checks; confirm the model version and source limits before use.
Write a work contract for {{GOAL}} with {{CONTEXT}}, deliver exactly {{OUTPUT_CONTRACT}}, and validate it with {{COMPLETION_CHECK}}; route by risk and escalate when needed.Replace before running: {{GOAL}}, {{CONTEXT}}, {{OUTPUT_CONTRACT}}, {{COMPLETION_CHECK}}
Turn a recurring task into a routable, testable work contract rather than a longer unstructured prompt.
Use GPT-5.6 Luna with the goal, context, deliverable, verification rule, and model-routing policy; keep side effects outside the initial draft.
Write the goal and context, state the exact deliverable, add a verification condition, and define when to route to another model or a human. Run one representative case and inspect the contract fields.
Accept a contract whose route can be explained, whose deliverable is testable, and whose escalation trigger is explicit. If a field is vague, ask for a concrete example before execution.
This is an editorial adaptation of a public work-contract article; it does not establish universal routing gains or current model performance.
This long-form article for marketing, writing, and development workflows reframes “prompting techniques” as a work contract: define Goal, Context, Output, Boundaries, and Completion check, then choose Luna, Terra, or Sol according to task risk. It recommends Luna for extraction, classification, transformation, and high-frequency tasks; higher-capability models should handle complex judgment. It also emphasizes that “reasoning effort is a compute budget, not a truth switch.”
Goal: Produce a source-backed decision guide for marketing leaders choosing among GPT-5.6 Sol, Terra, and Luna.
Use current OpenAI documentation for product facts. Use practitioner and community reports only as labelled experience, not proof. Compare the models by task risk, latency, cost, review burden, and accepted outcome.
Deliver a publish-ready article with a decision table, role-specific workflows, prompt templates, limitations, and FAQs. Preserve source links next to supported claims. Do not invent benchmarks, access rules, or usage limits.
Before finishing, verify that availability and pricing are current, each workflow has a human approval boundary, and the recommendation lets a reader choose a model.| Workload | Starting model | Reasoning level | Rationale |
|---|---|---|---|
| Quick rewriting, tagging, extraction, classification | Luna | none/low | Fast, inexpensive, easy to verify |
| Routine research, briefs, first drafts, repository exploration | Terra | low/medium | Balance of capability and cost |
| Important articles, strategy, complex debugging, synthesis | Sol | medium/high | Better suited to weighing cross-cutting constraints and evidence |
| High-risk investigations or architecture decisions | Sol | xhigh/max | Increase exploration only when results can be verified |
The article’s most valuable idea to carry over to Luna workflows is not a particular role instruction, but that “the completion standard comes before style requirements”: tell the model what to deliver, what to base it on, what it must not do, and what to check before finishing. For high-frequency tasks, first test the acceptance rate with Luna at low or none, then upgrade the model or reasoning level based on the type of failure.
The following is the main visible body text extracted through the Tabbit international app; the original English has been retained, while navigation and unrelated footer content have been omitted.
The practical GPT-5.6 question is no longer “Which model is smartest?” It is: which model, reasoning effort, product surface, and workflow will produce an accepted result without wasting time, tokens, or human review?
The answer is not a longer “magic prompt.” It is a clearer work contract and deliberate routing across Sol, Terra, and Luna.
GPT-5.6 works best when you define:
Goal: the result the user should receive or the state that should exist. Context: the sources, files, examples, data, and prior decisions that can change the answer. Output: the artifact, audience, format, depth, and required evidence. Boundaries: facts that must remain unchanged, actions that require approval, and conditions that should stop the work. Completion check: what the model must verify before it finishes.
The important change is philosophical: describe the destination precisely without narrating every footstep. Keep the constraints that protect the outcome, but remove repeated instructions, obsolete workarounds, irrelevant examples, and tools the task does not need.
Old prompting pattern
Think step by step. Search everything. Be exhaustive. Check every possible source. Create a plan, then make another plan. Do not stop until perfect. Ask before every step. Explain everything you do. Be concise. Be extremely detailed.
This prompt is contradictory, gives no quality threshold, and encourages unnecessary work.
GPT-5.6 work contract
Goal: Produce a source-backed decision guide for marketing leaders choosing among GPT-5.6 Sol, Terra, and Luna. Use current OpenAI documentation for product facts. Use practitioner and community reports only as labelled experience, not proof. Deliver a publish-ready article with a decision table, role-specific workflows, prompt templates, limitations, and FAQs. Preserve source links next to supported claims. Do not invent benchmarks, access rules, or usage limits. Before finishing, verify that availability and pricing are current, each workflow has a human approval boundary, and the recommendation lets a reader choose a model.
Quick rewrite, tagging, extraction, and classification should start with Luna at none or low. Routine research, briefs, first drafts, and repository exploration should start with Terra at low or medium. Important writing, strategy, ambiguous debugging, and complex synthesis should start with Sol at medium or high.
Reasoning effort is a budget, not a quality badge. Use the lowest setting that repeatedly passes your acceptance test. More reasoning can improve the work, but it can also increase latency, expand scope, and produce a more persuasive wrong answer.
The best unit of comparison is not cost per message. It is total workflow cost divided by accepted, verified outcomes. Total cost includes model usage, searches, tools, failed attempts, human review, correction, and the cost of a bad decision.
DMarketer Tayeeb · Source date: Not disclosed · Edited: 2026-09-20
Read the original sourceGPT-5.6 Luna
Run this guide in the environment listed above. Downloading does not transfer the template or establish model availability for your account.