Let’s be real for a moment and admit that your AI prompts are probably poorly written.
They’re costing your organization tokens, time and make everyone frustrated.
How to use this article
If you’re C‑level or VP / Director: focus on sections “Why prompts matter for the business”, “A shared prompt model”, “Prompts for C‑level” and “From prompts to team assets”.
If you’re in Product (PM/PO, UX, Research): focus on “A shared prompt model”, “Prompts for product teams” and “From prompts to team assets”.
If you’re an Engineer or Engineering Manager: focus on “A shared prompt model” and “Prompts for engineers”.
Skim the sections that match your role and copy-paste the prompt templates that fit your day to day work. I am sure this will improve the results!
Why prompts matter for the business (not just developers)
Executives are pouring budget into AI, but most organizations still interact with it like a slightly smarter search box. That means low quality prompts, hallucinated answers, “vibe-driven” decisions and disappointed leadership when the AI investment doesn’t move real metrics.
Studies on generative AI adoption in the C‑suite show expectations are high: leaders want faster decision cycles, leaner teams and accelerated software delivery. You will only get that if people across the organization learn to talk to AI like a colleague who needs a clear brief, not a magical mind flayer (Yes, Stranger things finale was awful).
Bad prompts usually share a few traits:
Vague requests (“Write a spec”, “Fix this bug”, “Give me some ideas”)
No context (no product, no customer, no tech stack, no constraints)
No desired format (so you get walls of text that nobody reads)
No iteration (first answer is treated as final instead of a draft to refine)
Fix your prompts and AI will become a force multiplier for strategy, product thinking and engineering execution.
You do not need “magic prompts”. You need a simple, shared structure that everyone can use. Many good guides for PMs and devs converge on the same pattern.
A practical model you can adopt:
Task – What do you want? (summarise, compare, draft, design, debug, etc.)
Context – Background the AI cannot guess (product, users, stack, constraints, examples)
Persona – Who should the AI “be”? (CIO advisor, senior PM, staff engineer, UX researcher)
Format – How should the answer look? (table, bullet list, architecture diagram description, PRD outline, step-by-step plan)
Tone / constraints – Level, depth and guardrails (assume non-technical audience, show risks, avoid hallucinating data, ask for missing info)
You can see similar components in PM-focused prompt guides and libraries.
Bad prompt:
“Explain microservices.”
Better prompt (copy-paste template):
“Act as a senior software architect at a B2B SaaS company.
Task: Explain microservices to a non-technical COO who understands finance but not engineering.
Context: We currently have a monolith in .NET serving ~50 mid-market customers, and we’re considering breaking out a few domains.
Format: 5 bullet points: what microservices are, 3 pros, 3 cons, and 2 concrete risks relevant to our situation.
Tone: No jargon; connect each point to business impact (cost, risk, speed).”
This same skeleton works for executives, PMs, and engineers; only the persona, context, and format change.
Prompts for C‑level & leadership
Strategic discovery & decision support
Executives often ask AI high-level, underspecified questions (“Should we adopt X?”) and get back generic blog content. The fix is to bind prompts to your constraints, strategy, and risk appetite.
Example 1 – Build vs buy evaluation
“Act as a CIO advisor experienced in SaaS and enterprise software.
Task: Evaluate build vs buy for an internal analytics dashboard for our product.
Context:
We are a mid-size B2B SaaS company in [industry]
Team: 20 engineers, 3 PMs; current stack is React front-end, Node.js backend, Postgres
Requirements: role-based access, SSO, self-serve filters, basic charts
Constraints: we want first release in 4 months, and we are sensitive to vendor lock-in
Format: A 3-column table (Option, Pros, Cons), followed by a short recommendation and 3 key assumptions we should validate
Tone: Advise like a management consultant; be explicit about risks and unknowns.”
You can link this kind of prompt to C‑suite AI adoption pieces that discuss decision-quality and risk.
Example 2 – Portfolio reprioritisation
“You are a Chief Product Officer coach.
Task: Help me reprioritise a product roadmap.
Context:
Market: [describe briefly].
Current roadmap: [paste list of initiatives with one-line descriptions].
Strategy: prioritize retention over new logo growth for the next 12 months.
Format:
Group initiatives into Must‑do, Nice‑to‑have, Defer with reasoning.
For each Must‑do, list 2 measurable outcomes we should track.
Tone: Challenge my assumptions; call out anything that doesn’t align with the stated strategy.”
Example 3 – Policy and guardrails draft
“Act as a VP of Engineering who has successfully rolled out AI usage guidelines.
Task: Draft an internal one-page AI usage guideline for product and engineering.
Context:
We build B2B software; we handle moderately sensitive customer data (no health or financial records).
We use hosted LLM tools and want to avoid leaking customer secrets or proprietary algorithms.
Format:3 ‘Do’ bullets.
3 ‘Don’t’ bullets.
A short section on code review expectations for AI-generated code.
Tone: Practical and concise; written for busy engineers and PMs.”
Prompts for product teams (PM, PO, UX, Research)
Product teams are perfectly positioned to benefit from AI, but generic prompts like “Write a PRD” usually produce generic products. Good prompts embed your customers, constraints and product strategy.
Learning concepts & market context
Example 1 – Market landscape briefing
“Act as a product strategist.
Task: Summarise the current landscape for [market or category]
Context:
Our product: [1–2 sentences]
Target segment: [SMB/enterprise/consumer, region]
Key competitors I care about: [list]
Format:5 bullet overview of the market
A table with rows = competitors (including us), columns = target segment, main differentiator, pricing rough order, go-to-market approach
3 open questions I should investigate further with real data
Tone: Concise and hypothesis-driven; avoid making up hard numbers and clearly flag speculation.”
Example 2 – Explain a technical concept in product terms
“You are a senior PM who has a technical background.
Task: Explain event-driven architecture to a non-technical stakeholder.
Context: Our app currently uses a monolithic design and we’re exploring events to improve decoupling between billing, notifications, and reporting.
Format:
A short analogy that a salesperson would understand
3 benefits tied to product outcomes (faster iteration, fewer regressions, etc.)
3 risks or costs we need to be aware of
Tone: No diagrams, no code; focus on product and business impact.”
Example 3 – Self-study / learning plan
“Act as a product coach.
Task: Create a 4‑week learning plan for me to understand [topic: pricing, experimentation, growth loops, etc.].
Context:
Role: [PM/PO level, domain]
Time: 3 hours per week
I learn best by doing small exercises
Format: Weekly plan with: concept focus, 1–2 key resources to search for (don’t paste full content), and 1 practical exercise I can do in my current product
Tone: Practical, realistic, not academic.”
Discovery & research
Example 4 – Synthesizing user interviews
“You are a UX researcher.
Task: Analyse these [N] interview transcripts.
Context:
Product: [short description]
Target users: [persona]
Goal of the study: understand why users churn after the trial
[Paste anonymised interview snippets.]
Format:5 top recurring pain points with user quotes
3 surprising insights
3 hypotheses we should test next
Tone: Neutral and evidence-based; do not speculate beyond what users actually said.”
Example 5 – Generating interview questions
“Act as a senior UX researcher.
Task: Draft a semi-structured interview guide.
Context:
Objective: understand how [target persona] currently solves [problem]
Constraints: 30‑minute calls; remote only
Format:3 warm-up questions
8–10 core questions, grouped into themes (current workflow, tools, pain points, decision criteria)
3 wrap-up questions
Tone: Open-ended, non-leading; avoid suggesting solutions.”
Example 6 – Product discovery prompts
“You are a product discovery coach.
Task: Help me explore new opportunities around [problem space].
Context:
Our current product: [1–2 sentences]
Customers: [segment]
Known pain points: [bullets]
Format:5 ‘How might we…’ opportunity statements
For each, 2 example experiments we could run in <4 weeks to learn more
Tone: Creative but grounded; avoid over-engineered solutions.”
Shaping features & specs
PM prompt libraries often provide templates for PRDs and user stories.
Example 7 – From idea to user stories
“Act as a senior product manager.
Task: Turn this vague feature idea into clear user stories.
Context:
Product: [brief description]
Idea: [paste your rough idea]
Users: [persona]
Format:1‑sentence feature summary
5–8 user stories in ‘As a… I want… so that…’ format
For each story, 3 acceptance criteria
Tone: Practical and implementation-ready.”
Example 8 – Challenging a spec
“You are a staff engineer reviewing a PRD.
Task: Find ambiguities, hidden complexity, and missing edge cases.
Context: [Paste PRD or key sections.]
Format:
A bullet list of questions we must answer before development
Edge cases grouped by flow
Any risky assumptions you see
Tone: Constructive but direct, as if commenting on a real document.”
Example 9 – Writing a PRD skeleton
“Act as a senior PM at a B2B SaaS company.
Task: Create a PRD outline for a new [feature type] that solves [problem] for [persona].
Context: We target [segment] and our product currently [short description].
Format: A PRD template with sections for: context, goals, non-goals, user stories, UX notes, tech considerations, rollout, and success metrics, each with example prompts or questions for me to fill in.
Tone: Clear and concise; designed to be reused as a template.”
Prompts for engineers: concepts, architecture, code, debugging
Developers have the most to gain from good prompts, but also the most to lose from sloppy ones, because mistakes turn into production issues. Many technical prompt guides show the same core best practices: be specific, provide context and iterate.
Learning technical concepts
Example 1 – Concept explanation at your level
“Act as a senior backend engineer.
Task: Explain eventual consistency.
Context:
I am a mid-level engineer familiar with relational databases and basic transactions.
Our stack: [stack].
Format:Short definition.
Example using a familiar system (e.g., user profile updates).
3 pros and 3 cons, tied to user experience and failure modes.
Tone: Assume I can read code but haven’t worked with distributed systems in depth.”
Example 2 – Compare two approaches
“You are a staff engineer.
Task: Compare using WebSockets vs long polling for real-time updates in our app.
Context:
Stack: [e.g., Node.js backend, React frontend].
Scale: ~5k concurrent users at peak.
Format:Table with rows = dimension (latency, complexity, infra, cost, browser support, failure handling).
A recommendation for our context, with 2 scenarios where the other option might be better.
Tone: Balanced; avoid blanket ‘X is always better’ statements.”
Example 3 – Practice / quiz
“Act as an educator preparing me for a technical interview.
Task: Create a short quiz about REST vs GraphQL.
Context: I’m applying for a senior frontend role and will work with both APIs.
Format:
10 mixed questions (multiple choice + open-ended).
Answer key.
For each open-ended question, a sample ‘good’ answer.
Tone: Focus on practical trade-offs, not trivia.”
Architecture and design
Architecture prompts should always include non-functional requirements and constraints.
Example 4 – Designing a service
“You are a principal architect.
Task: Propose a high-level architecture for a notification service.
Context:
We need to send email and in-app notifications.
Volume: up to 200k notifications per day.
Stack: Kubernetes, Node.js, Postgres, existing event bus (Kafka).
Requirements: retries, idempotency, user-level preferences.
Format:Textual architecture diagram: components and how they communicate.
3 main data models.
5 failure modes and how to handle them.
Tone: High-level; avoid low-level implementation details.”
Example 5 – Reviewing an existing design
“Act as a critical design reviewer.
Task: Review the following architecture and point out risks and alternatives.
Context: [Paste your design or summary.]
Format:
List of concerns grouped by category (scalability, reliability, security, complexity)
For each concern, 1–2 possible mitigations
Any over-engineering we might be doing
Tone: Honest and pragmatic, as if for a design review meeting.”
Example 6 – NFR-focused prompt
“You are a site reliability engineer.
Task: Given this system description, list non-functional requirements we should explicitly capture.
Context: [description].
Format: Table with NFR (availability, latency, throughput, etc.), suggested target, and 1–2 notes on how to measure it.
Tone: Opinionated but realistic for a mid-size SaaS company.”
Feature and functionality discussion (engineering lens)
Example 7 – Refining requirement from engineering side
“Act as a senior engineer discussing scope with a PM.
Task: Analyse the following feature request and highlight technical trade-offs and complexity drivers.
Context: [paste feature description or user stories].
Format:
Sections: unclear requirements, hidden complexity, dependencies, potential performance issues
For each, suggestions for how to simplify or de-risk
Tone: Collaborative, not defensive.”
Example 8 – Edge cases and constraints
“You are a backend engineer.
Task: For the password reset flow described below, list edge cases and constraints we should handle.
Context: [paste brief flow].
Format:
Bullet list of edge cases grouped by step
Any security or privacy concerns
3 logging or monitoring events we should add
Tone: Security-conscious and thorough.”
Example 9 – API contract refinement
“Act as an API designer.
Task: Turn this rough idea into a clean API contract.
Context: [describe the operation].
Format:
REST endpoint(s) with methods and URLs
Request and response JSON schemas
Error handling strategy
Tone: Idiomatic to our stack and aimed for simplicity.”
Technical research & discovery
Example 10 – Library / tool comparison
“You are a senior engineer choosing a library.
Task: Compare 3 libraries for [purpose].
Context:
Language and stack: [details].
Constraints: must be open source, compatible with [version], and well-maintained
Format:Table with rows = library, columns = pros, cons, community activity, docs quality, risk notes
Short recommendation with 2 things we must test in a spike
Tone: Cautious; clearly flag places where real benchmarking is needed.”
This aligns with how dev focused prompt guides suggest requesting structured comparisons and caveats.
Example 11 – RFC first draft
“Act as a staff engineer.
Task: Draft a 1‑page RFC skeleton for introducing feature flags.
Context: [describe current deployment setup and problem].
Format: Sections for: context, goals, non-goals, proposed solution, alternatives considered, rollout plan, risks, open questions.
Tone: Short and structured; meant to be edited by humans.”
Example 12 – Generating follow-up questions
“You are mentoring a junior engineer doing technical research.
Task: Given this summary of [topic], generate follow-up questions we should answer before choosing an approach.
Context: [paste summary].
Format:
10–15 questions grouped by categories (performance, security, maintainability, team skills, operational impact)
Tone: Socratic; designed to deepen our understanding.”
Code generation
Developer prompt guides are clear: specify language, version, style, constraints and tests.
Example 13 – Function-level generation
“Act as a senior [language] engineer.
Task: Implement a pure function.
Context:
Language: [e.g., TypeScript]
Function: filterAndSortActiveUsers(users: User[]): User[]
Requirements:
Only include users with active === true
Sort by lastLogin descending
Do not mutate the input array
Format:
Code only, no explanations
Then a short separate section with 3 unit tests using [test framework].
Tone: Idiomatic and clean.”
Example 14 – Scaffolding with explanation
“You are a senior full-stack engineer.
Task: Scaffold an Express.js route for uploading profile pictures to S3.
Context:
Stack: Node.js 20, Express 4, AWS SDK v3
Requirements: max 5MB, only JPEG/PNG, store under /users/{userId}/ path
Format:Route handler code
A brief explanation of key security considerations (validation, auth, presigned URLs)
Tone: Production-minded, not toy examples.”
Example 15 – Iterative refinement
Start with a basic prompt, then follow up:
“Generate a first draft implementation of [feature] given these requirements: [paste].”
“Now act as a code reviewer. List issues, smells, and missing edge cases in the code above.”
“Apply your own review comments and produce an improved version. Then generate unit tests.”
Several “prompt engineering for programmers” guides demonstrate this iterative review-and-improve loop.
Debugging
Debugging prompts work best when they include the error, the context, and what you’ve already tried.
Example 16 – Debugging with context
“Act as a senior [language/framework] engineer.
Task: Help debug this issue.
Context:
Environment: [OS, runtime, framework versions]
What I’m trying to do: [1–2 sentences]
Error message: [paste]
Relevant code: [paste minimal snippet only]
What I already tried: [bullets]
Format:3–5 hypotheses ranked by likelihood
For each, what to inspect or log next
Tone: Step-by-step; do not just guess a fix without explaining reasoning.”
Example 17 – Minimal reproducible example
“You are my pair programmer.
Task: Turn the following bug description into a minimal reproducible example and a failing test.
Context: [describe bug and relevant code].
Format:
The smallest possible code snippet that shows the bug
A failing unit test using [framework]
Tone: Focused on clarity and isolation, not on full application context.”
Example 18 – Logging and observability
“Act as an SRE.
Task: Suggest a logging and monitoring strategy for this endpoint that’s causing intermittent timeouts.
Context: [endpoint description, stack, timeout symptoms]
Format:
List of key metrics and logs to capture (with example log lines or metric names)
3–5 steps for investigating future incidents
Tone: Practical and prioritised; assume limited time.”
If you stop here, each person will have a few better chats, but the organisation will not fundamentally change. To make AI actually pay off, turn good prompts into shared assets.
Practical steps:
Create a prompt library: a simple Notion page, Confluence space, or even a Microsoft List where you store role specific prompt templates (C‑level, PM, Eng) and update them over time
Version and review prompts: treat them like lightweight specs, evolve them based on what worked in real decisions, discovery and delivery
Educate by example: link out to existing prompt collections and guides for PMs and engineers and adapt the best ideas to your context
Your AI investment will only compound if everyone, from the boardroom to the IDE learns to brief AI with the same clarity you expect in any good product spec or engineering ticket.
If you want to read more articles like this, you can do so here: https://code-of-us.beehiiv.com.