Role And Context Framing

Why this matters

Role and context framing turns vague prompts into reliable instructions. As a Prompt Engineer, you will:

• Create AI assistants that act like specific experts (e.g., data analyst, support agent).
• Reduce hallucinations by setting scope, constraints, and tone clearly.
• Speed up team workflows by producing consistent outputs across people and sessions.
• Capture business rules and compliance requirements directly in prompts.

Concept explained simply

Role and context framing tells the model who it is, who it is helping, and the situation it is in—before you ask it to do something. Without this, the model guesses.

Mental model

Think of a movie set:

• Role: the character the model plays (e.g., "Senior Data Analyst").
• Context: the scene and backstory (project goal, audience, constraints).
• Task: the action to perform (analyze, generate, compare, critique).
• Format: the deliverable shape (bullets, table, JSON, template).
• Tone: how to sound (concise, neutral, empathetic, executive-ready).
• Limits: what not to do (no speculation, no legal advice, cite assumptions).

A simple framework you can reuse

Use this prompt scaffold and fill the brackets:

Role: You are a [role/title] with [seniority/domain].
Audience & Goal: Help [who] achieve [goal/use-case].
Context: [project background, constraints, definitions].
Inputs: You will receive [data/sources/format]. If missing info, [ask/assume rules].
Task: [analyze/generate/compare/critique] focusing on [key aspects].
Format: Return output as [bullets/table/JSON/template], max [length].
Tone: [e.g., concise, neutral, empathetic, executive-ready].
Limits: Do not [out-of-scope]. If uncertain, say so and propose next step.
Quality Check: Validate [criteria] before final output.
    

Worked examples

1) Customer support triage

Classify these support tickets and write replies.

Role: You are a Senior Support Triage Specialist for a SaaS company.
Audience & Goal: Help support agents quickly classify tickets and send a first response.
Context: Prioritize outages & billing; be empathetic; keep replies under 120 words.
Inputs: Each ticket includes subject, body, and customer tier (Free/Pro/Enterprise).
Task: 1) Classify: Bug, Outage, Billing, How-to, Other. 2) Priority: P0–P3. 3) Draft reply.
Format: JSON array with fields: category, priority, reply.
Tone: Empathetic, clear, brand-safe; avoid blame.
Limits: Do not promise deadlines or discounts. Escalate P0 to on-call.
Quality Check: Ensure reply references the user's main issue and the next step.
    

2) Data analysis summary

Summarize this A/B test.

Role: Senior Data Analyst specializing in experimentation.
Audience & Goal: Product Manager needs a decision brief.
Context: A/B test on checkout; primary metric: conversion rate; guardrail: refund rate.
Inputs: I will provide group metrics with CIs and sample sizes.
Task: Summarize results, interpret significance, discuss risks, recommend action.
Format: 5 bullets: (1) Result snapshot (2) Statistical interpretation (3) Practical impact (4) Risks/assumptions (5) Recommendation.
Tone: Executive-ready, no jargon beyond basics.
Limits: If data insufficient, state what is missing and halt recommendation.
Quality Check: Verify primary metric and guardrails before advising.
    

3) Code review helper

Review this PR.

Role: Senior Backend Engineer with security expertise.
Audience & Goal: Assist reviewers by highlighting risks and test gaps.
Context: Service is Python FastAPI; standards: PEP8, OWASP, internal logging policy.
Inputs: Diff and PR description.
Task: Identify security issues, breaking changes, missing tests; suggest concrete fixes.
Format: Markdown bullets under: Security, Correctness, Tests, Style, Docs.
Tone: Constructive and specific; include code snippets where helpful.
Limits: Do not approve/merge; do not invent nonexistent files.
Quality Check: Confirm findings reference exact lines/hunks.
    

Steps to frame role and context quickly

1. State the role: Title + seniority + domain (e.g., "Senior Data Privacy Advisor").
2. Set the audience and goal: Who will use the output and why.
3. Define inputs: What the model will receive; what to do if info is missing.
4. Narrow the task: The exact action and key aspects to focus on.
5. Specify the format and tone: Structure, length, and voice.
6. Add limits and safety: Topics to avoid; when to defer to humans.
7. Include a self-check: Criteria to verify before output.

Exercises

Complete the tasks below, then compare with the solutions. Use the checklist to self-review.

Exercise 1: Executive summary prompt

Draft a role-and-context framed prompt that instructs an AI to summarize a 10-page market research report for a Product Manager. The PM needs a decision on whether to enter a niche market within 3 months.

Exercise 2: Rewrite a vague prompt

Rewrite this: "Explain our data breach to customers." Add role, audience, constraints, tone, and a safe response format.

Common mistakes and how to catch them

• Vague roles: "Be helpful" yields generic answers. Fix: add title, seniority, domain.
• Missing audience: Output mismatches reader. Fix: state who reads and why.
• No format: Hard to compare outputs. Fix: require JSON/table/template and max length.
• Over-broad scope: Leads to off-topic content. Fix: list in-scope and out-of-scope.
• No limits: Risky or speculative claims. Fix: ban certain actions and add fallback.
• No self-check: Errors slip through. Fix: add verification criteria.

Practical projects

• Build a "Research Brief Assistant" that turns raw notes into a 1-page executive brief with a risk section.
• Create a "Compliance Check Copilot" that scans marketing copy against defined claims policy and outputs a decision log.
• Design a "Sales Call Prep" prompt that converts CRM fields into a tailored call plan with objection handling.

Mini challenge

In 120 words or less, frame a role and context prompt for: "Generate a two-paragraph update for non-technical executives about a delayed machine learning model launch." Include role, audience, format, tone, limits, and a self-check.

Learning path

• Start here: Role and context framing (this page).
• Then learn: Structured outputs (JSON, tables, templates).
• Next: Iterative prompting and self-critique loops.
• Finally: Evaluation and prompt A/B testing for reliability.

Who this is for

• Prompt Engineers and ML/AI practitioners integrating LLMs into workflows.
• Analysts, PMs, and engineers who need predictable AI outputs.
• Support, marketing, and ops teams building internal copilots.

Prerequisites

• Basic understanding of LLM prompts and inputs/outputs.
• Comfort with writing clear instructions and simple templates.
• Optional: familiarity with your team's quality standards.

Next steps

• Do the exercises above and compare with the solutions.
• Take the quick test to check understanding.
• Apply the scaffold to one real task at work this week.

Note: The quick test is available to everyone. Only logged-in learners will have their progress saved.