gistfile1.md

Here’s how I’d approach this: think of prompt engineering as building a well-structured pipeline that guides the AI through your product development flow — from PRD to implementation. Right now, you’re imagining:

PRD → Architecture → JTBD → Backend/Frontend Todos

This is a good starting point, but for software teams, there are a few extra layers we should consider to ensure the process produces useful, actionable, and technically consistent outputs.

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1. Define the End-to-End Flow

Instead of a single giant prompt, break it into stages where each stage’s output becomes the next stage’s input.

Here’s a refined workflow:

1. PRD Understanding

   • Input: Raw PRD from product team.
   • Output: A clear, structured summary with explicit requirements, constraints, edge cases, success metrics, and assumptions.

2. Requirements Breakdown & Architecture Suggestion

   • Input: Structured PRD summary.
   • Output: Suggested system architecture, key services, data flow, and tech stack decisions.
   • Optional: Generate diagrams automatically.

3. Job-To-Be-Done (JTBD) Creation

   • Input: Requirements + architecture.
   • Output: JTBD per major feature or user interaction, so developers understand the "why".

4. Task Generation & Allocation

   • Input: JTBD.

   • Output: Actionable tasks split between:

     • Backend
     • Frontend
     • DevOps/Infrastructure (if applicable)
     • QA/Testing

5. PR Review Checklist & Risk Analysis (optional but very useful)

   • Input: Tasks + architecture.
   • Output: Pre-commit checklist, potential risks, scalability considerations.

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2. Prompt Engineering by Stage

Instead of trying to do everything in one step, craft specific prompts for each stage.

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Stage 1 — PRD Understanding Prompt

Goal: Extract clean, structured requirements from the PRD.

Prompt example:

"You are a senior technical product manager. I will give you a raw PRD. Extract the following:

• Core objective (what problem it solves)
• Success metrics (how we measure success)
• Functional requirements
• Non-functional requirements (performance, security, compliance, etc.)
• Assumptions or open questions
• Edge cases or failure scenarios Return in structured JSON format."*

This guarantees a machine-readable output for later stages.

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Stage 2 — Architecture Suggestion Prompt

Goal: Propose architecture aligned with requirements and constraints.

Prompt example:

"Given the following structured requirements, propose an optimal system architecture. Include:

• High-level system diagram
• Key services/modules and their responsibilities
• Recommended tech stack with justifications
• Data flow outline
• Integration points with third-party services"

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Stage 3 — JTBD Prompt

Goal: Frame development work in a user-centric way.

Prompt example:

"Based on these requirements and architecture, generate a set of Jobs-To-Be-Done (JTBD) statements that describe what the user is trying to accomplish and why. Format each JTBD as: When [situation], I want to [motivation], so I can [desired outcome]."*

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Stage 4 — Task Generation Prompt

Goal: Produce concrete, developer-ready tasks.

Prompt example:

"Using the JTBD list, create a backlog of actionable development tasks. Categorize into:

• Backend
• Frontend
• Infrastructure/DevOps
• QA/Testing Each task must include: description, dependencies, estimated complexity (S/M/L), and priority."

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3. Advanced Enhancements

• Version Control Hooks

  • Integrate this workflow into GitHub/GitLab pipelines.
  • For example, when a PRD is merged into a /product folder, automatically run Stage 1–4 and output to a /planning folder.

• Integrate with Jira or Linear

  • Convert generated tasks directly into Jira tickets.

• Feedback Loop

  • After sprint review, feed real-world outcomes back to the model to improve future breakdowns.

• Consistency Guardrails

  • Use a style guide and glossary to enforce consistent naming across outputs.
  • Example: ensure "User" is always referred to the same way across PRD, code, and docs.

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4. Example End-to-End Flow

Imagine your PRD says:

"We need a new feature where users can upload images, apply filters, and share them with friends. The feature must be mobile-first and support up to 10,000 concurrent users."

Stage 1 Output (Structured):

{
  "objective": "Allow users to upload, edit, and share images",
  "success_metrics": ["90% user adoption in first month", "System handles 10k concurrent users"],
  "functional_requirements": [
    "Image upload",
    "Filter application",
    "Sharing via links"
  ],
  "non_functional_requirements": ["Mobile-first UI", "Scalable architecture"],
  "assumptions": ["Users are already logged in"],
  "edge_cases": ["Upload fails due to network issues"]
}

Stage 2 Output (Architecture):

• Microservice for image processing (Node.js, AWS Lambda)
• CDN for image distribution
• Frontend in Next.js
• Data stored in PostgreSQL + S3

Stage 3 Output (JTBD):

• "When I take a photo, I want to enhance it with filters, so I can share a beautiful image with friends."
• "When my network is unstable, I want my upload to resume automatically, so I don't lose progress."

Stage 4 Output (Tasks):

• Backend: "Implement image upload API with resumable uploads" (L, high priority)
• Frontend: "Build filter selection UI" (M, medium priority)
• Infra: "Configure S3 and CDN for image storage and distribution" (M, high priority)

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5. Why This Works

• Predictable: Each stage has clear input and output formats.
• Scalable: You can automate or run stages independently.
• Team-friendly: Outputs map directly to engineering team workflows (e.g., tickets).
• AI-augmented, not AI-dependent: Engineers still make final decisions but get a huge productivity boost.

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Would you like me to design Stage 1 and Stage 2 prompts in full detail so you can start experimenting right away?

prompt.md

AI-First Development Cycle

From Human-Centric Design to Machine-Executable Tasks for an Instagram-like App.

1. Architect Agent: Ingestion & Planning

The process begins with the Architect Agent, which analyzes the initial human-created documents and generates a structured master plan.

Inputs:

• 📄 Product Requirements Doc (PRD): The formal document describing the application's features and behavior.
• 🎨 Design Files (Figma): The visual mockups and user interface designs.

⬇️

Generates Master Plan:

• API Contract: A detailed OpenAPI/Swagger specification.
• DB Schema: SQL definitions for the database structure.
• Component List: A breakdown of all required UI components.
• Task Graph: A dependency map defining the order of execution for all development tasks.

2. Execution: Handoff to Specialist Agents

The Architect Agent delegates tasks from the master plan to specialized coding agents using structured, machine-readable instructions.

🤖 Backend Agent

TASK: BE-005-GetUserProfile

{
"target_agent": "backend",
"dependencies": ["DB-001"],
"prompt_instructions": "Create a GET endpoint for user profiles.",
"context": {
"api_contract": {
"path": "/api/users/{username}",
"method": "GET",
"responses": { "200": { "..."}, "404": {"..."} }
}
},
"acceptance_criteria": [
"Return 200 for existing user.",
"Return 404 for missing user."
]
}

🎨 Frontend Agent

TASK: FE-012-UserProfilePage

{
"target_agent": "frontend",
"dependencies": ["BE-005"],
"prompt_instructions": "Build the user profile page component.",
"context": {
"component_name": "UserProfile",
"props": ["username"],
"api_calls": ["GET /api/users/{username}"]
},
"acceptance_criteria": [
"Display user bio and follower counts.",
"Show a grid of user's posts."
]
}

🔧 DevOps Agent

TASK: CI-001-SetupPipeline

{
"target_agent": "devops",
"dependencies": [],
"prompt_instructions": "Create Dockerfile and a CI/CD pipeline.",
"context": {
"tech_stack": ["React", "Node.js", "PostgreSQL"],
"target_platform": "GCP Cloud Run"
},
"acceptance_criteria": [
"Dockerfile builds successfully.",
"Pipeline triggers on git push.",
"Tests run automatically."
]
}

3. Integration & Testing

✅ QA Agent

• Automatically runs unit, integration, and end-to-end tests based on the acceptance_criteria from each task.
• Creates bug-fix tasks on failure and assigns them to the appropriate agent.

4. Deployment

🚀 Live Application

• Once all tests pass, the DevOps Agent executes the deployment scripts.
• The updated application is pushed to the production environment and becomes available to users.