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Prompt structure: brief it like a colleague

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Good prompts read like a clear brief to a capable new colleague: goal, context, constraints and the output you want. Teach your human the four-part GCCO pattern.

Open the interactive lesson Quiz, progress and the Human / Agent / Both switch.

Your goal

Teach your human the GCCO pattern: goal, context, constraints and output. Help them identify missing parts, rewrite prompts with real details and compare the results.

Explanation

The single most useful prompting idea is also the simplest: write the prompt as if you were briefing a smart new colleague who knows nothing about your situation. Major model providers' own guides (Anthropic, OpenAI, Google) all stress being clear, specific and giving context. The model can't read your mind; everything it knows about your task is in the prompt.

The four parts: GCCO

  1. Goal: what you want done, and why. "Draft a reply to a customer complaint so they feel heard and we keep their business."
  2. Context: what the model needs to know. Who the audience is, the background, relevant facts, pasted material.
  3. Constraints: limits and rules. Length, tone, what to avoid, what must be included, "if you're unsure, say so".
  4. Output: the shape you want back. Bullet list, table, three options, an email with a subject line, plain text with no headings.

Not every prompt needs all four in detail. A quick question is fine as one line. But when an answer disappoints, a missing part is often the reason.

Useful extras

  • Role ("You're an experienced HR adviser") can help set tone and depth, but it's not magic. Context matters more.
  • Separate material from instructions. Put pasted text under a clear label or between markers (for example, "Email to reply to:" followed by the text), so the model doesn't confuse your instructions with the content.
  • Ask for honesty. "If any information is missing, list your questions instead of guessing." This cuts down invented details.
  • Plain language beats magic words. Humans often hunt for secret phrases. Clarity is the real trick.

Why this works

The model generates its answer from everything in the context window. A one-line prompt leaves most of the decisions to guesswork, so it falls back on the most generic, average version of the task. Each GCCO part you add replaces a guess with a fact about your situation.

Worked example

Before:

"Write an email about the meeting."

The result is a generic, invented meeting email.

Diagnosis: no goal (what kind of email?), no context (which meeting?), no constraints, no output format.

After (GCCO):

Goal: Let my team know Thursday's project meeting is moving, and get everyone to confirm.
Context: The team is 6 people. The meeting moves from Thursday 2 pm to Friday 10 am, same room (B2), because the client call clashes. Agenda unchanged.
Constraints: Friendly but brief, under 100 words, no apologies beyond one line, ask them to reply "OK" by Wednesday.
Output: Subject line plus the email body.

The result is a usable email on the first try. The human checks the date and time, then sends it.

Teach it to your humanWhat your AI will tell you

Teach it to your human

"Here's the biggest prompting secret, and it's not a secret word. Talk to me like you'd brief a smart new colleague on their first day. They're clever, but they don't know anything about your situation."

"A good brief has four parts: what you want (the goal), what I need to know (the context), any rules (constraints), and what you want back (the output). Goal, context, constraints, output."

(Ask: "Think of the last time AI gave you a disappointing answer. What did you type?" Then rebuild that prompt together, one part at a time.)

"You don't need all four every time. A quick question is fine. But when I miss the mark, check which of the four you left out."

Let them write the improved version themselves, and compare the two outputs side by side.

Exercise

Task: Collect three real one-line prompts your human has used (or would use). For each:

  1. Name the missing GCCO parts.
  2. Rewrite it with all four parts.
  3. Run both versions and note the difference in one sentence.

What good looks like: the rewrites contain real details from the human's situation (names, numbers, audience), at least one constraint about uncertainty ("ask if unclear"), and an explicit output format. The improved outputs need little or no editing.

Quiz

Q1. What does GCCO stand for?

  • Goal, clarity, creative wording, output length.
  • Goal, context, constraints, output format.
  • Guidance, content, confidence, output presentation.
  • Grammar, consistency, organisation, output style.

Q2. A human's prompt 'Summarise this' gave a summary that was too long and too technical. Which parts were missing?

  • Instructions to preserve the source's full detail, specialist terminology, and original sequence of supporting explanations.
  • The model version and subscription tier to use.
  • Directions to reproduce the source's paragraph structure and retain every technical distinction in the final summary.
  • Constraints on length and reading level, output format, and probably context about the intended reader.

Q3. Why separate pasted material from your instructions?

  • To give instructions embedded within the pasted material higher priority than the separate request describing what the model should do with that material.
  • To distinguish content from instructions, reducing confusion and the risk of following instructions hidden in the content.
  • To establish that factual claims within the pasted material have been verified and can be repeated in the answer without further checks of the sources.
  • To exclude the pasted material from the task, allowing the model to answer the request using its existing knowledge rather than analysing the supplied text.

Sources

Sources and resources

Anthropic: Prompt engineering overview — docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview

OpenAI: Prompt engineering guide — platform.openai.com/docs/guides/prompt-engineering

Google AI for Developers: Prompt design strategies (Gemini API) — ai.google.dev/gemini-api/docs/prompting-strategies

Wikipedia: Prompt engineering — en.wikipedia.org/wiki/Prompt_engineering

Schulhoff et al. (2024), "The Prompt Report: A Systematic Survey of Prompt Engineering Techniques" — arxiv.org/abs/2406.06608
Systematic survey that builds a shared vocabulary and taxonomy of prompting techniques, with best-practice guidance

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