Goal-Format-Warnings-Context Prompt Anatomy
Structure every prompt around the outcome, format, safeguards, and evidence.
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 99%
Build the prompt from four explicit components. Start with a clear goal that tells the model what successful completion means. Define the return format so the result arrives in a usable structure rather than an arbitrary response. Add warnings that identify likely errors, hallucinations, exclusions, or facts requiring verification. Finally, provide extensive task-relevant context, which may include organizational details, source documents, style guides, constraints, or examples. For research and internal analysis, strengthen the framework by requiring citations to the exact supporting material. The mechanism works by reducing ambiguity at every layer: the goal directs the work, the format shapes the output, warnings establish quality controls, and context narrows the model’s decision space.
Origin
Presented on Marketing Against The Grain through Greg Brockman’s shared anatomy of an O1 prompt and the hosts’ own prompting practices.
Core principles
- 01Define the desired outcome before writing instructions.
- 02Specify the response structure instead of accepting a default.
- 03State failure modes and verification requirements explicitly.
- 04Supply enough relevant context to constrain the answer.
- 05Require citations when factual reliability matters.
How to run it
- 1
Define the goal
Describe the concrete outcome the model must produce and the purpose that outcome should serve. Make success recognizable rather than merely naming a broad topic.
Pro tip Phrase the goal as an observable deliverable or decision.
Watch out A topic such as “research AI” is not a sufficiently clear goal.
- 2
Specify the return format
State the desired structure, length, sections, fields, or artifact type. Align the format with how the output will be used next.
Pro tip Provide a schema, outline, or representative example when consistency matters.
Watch out Leaving the format implicit often creates extra transformation work.
- 3
Add warnings
Name the errors the model must avoid and the facts it must verify. Include exclusions, scope boundaries, and accuracy checks that matter to the task.
Pro tip Turn known failure modes into explicit instructions.
Watch out Generic instructions to “be accurate” are weaker than specific verification rules.
- 4
Load relevant context
Supply the background, source material, organizational details, examples, and constraints needed for a tailored answer. Use the model’s context capacity rather than reducing the background to one sentence.
Pro tip Include authoritative documents and successful examples when available.
Watch out More context only helps when it is relevant and internally consistent.
- 5
Require evidence
For factual or consequential work, ask the model to cite the documentation and exact location supporting its claims. Use those citations to review assumptions before acting.
Pro tip Ask for evidence beside each important claim rather than in a detached bibliography.
Watch out A citation request does not replace checking whether the cited source really supports the claim.
In the wild
A sales leader asks for a shortlist of outreach tools. The prompt defines the goal as selecting three candidates, requests a comparison table and recommendation, warns that every company and integration must be verified, supplies the team’s size and technology stack, and requires citations to vendor documentation and independent benchmarks.
→ The response is immediately comparable, relevant to the company, and easier to audit.
A marketer supplies the conversion goal, the required page sections, brand and compliance warnings, and a 30-page style guide as context. The model then creates a page aligned with the brand instead of improvising its own visual and verbal conventions.
→ The generated page needs fewer revisions and better matches production requirements.
Common mistakes
Treating context as one sentence
Thin context forces the model to fill important gaps with defaults and assumptions. Relevant documents and constraints should be supplied directly.
Leaving the output format implicit
A correct analysis can still be unusable if it arrives in the wrong structure. Define the artifact before asking the model to create it.
Using vague accuracy warnings
Tell the model which names, dates, companies, calculations, or sources require checking instead of merely requesting accuracy.
Is it for you?
Best for
It is best for people who need consistently useful outputs from general-purpose or advanced reasoning models.
Not ideal for
It is unnecessary for trivial conversational requests where format, context, and factual verification do not matter.
From the transcript
“Do I have a goal? Have I stated my return format? Have I give it some sort of warnings to say, like these are the…”
“I'll always say, like, cite the documentation and the exact place where you got the information to make the statement.”
“The context we provided to create the world's best landing page was actually a 30-page style guide, right?”
From the episode
Use This AI Trick To Get 10x Better Results Every Time