AI Prompt Templates for Software Development

This post gives ready-to-use AI prompts, recommended steps, and parameters you can copy‑paste to accelerate software development tasks — from design and coding to testing, documentation and automation. You have no input constraints; choose the prompt that fits your goal and adapt the parameters.

How to use these prompts (quick guide)

1. Pick the task you want (design, implement, test, refactor, document, automate).
2. Set parameters: language, framework, style, complexity, constraints (see "Parameters" below).
3. Run prompt in your AI assistant (paste the template and fill parameters).
4. Inspect & iterate: validate output, ask for changes, or request tests and examples.
5. Automate by integrating the prompt into CI, scripts, or a prompt-engineering file if you repeat the workflow.

General prompt structure (recommended)

Use this structure as the wrapper for any task:

You are an expert software engineer and AI automation specialist.
Goal: <describe the goal clearly>
Context: <brief system context, language, framework, constraints>
Inputs: <files, APIs, existing code, or "none">
Deliverables: <what you want: code, tests, docs, CI config, step-by-step plan>
Constraints: <compatibility, performance, security, licensing>
Style: <concise, commented, production-ready, modular>
Example output format: <file names, code blocks, bullet list plan>

Then: \ 

Prompt templates (copy & adapt)

1) Design system / architecture

You are an expert system architect.
Goal: Design a scalable backend for <feature/product>.
Context: Tech stack: <language, framework, DB, cloud provider>.
Constraints: <SLA, concurrency, cost limit, compliance>.
Deliverables:
 - high-level architecture diagram (components and interactions)
 - data model (tables/collections)
 - API endpoints (paths, request/response)
 - nonfunctional considerations (scaling, caching, monitoring)
Format: provide a short textual diagram and a Mermaid/PuML snippet.
  

2) Implement a feature (code + tests)

You are a senior backend developer and test engineer.
Goal: Implement <feature name>.
Context: Language: <e.g., Python 3.11, Node.js 20, Java 17>. Framework: <e.g., FastAPI, Express, Spring Boot>.
Inputs: <existing code files or "none">.
Deliverables:
 - production-ready function/class with docstring/comments
 - unit tests (framework: <pytest/jest/junit>)
 - example usage and expected output
Constraints: keep runtime complexity ≤ <O(...)> and do not add external deps unless approved.
  

3) Refactor & optimize

You are an expert refactorer.
Task: Improve readability, performance, and test coverage for the following code:
<paste code here>
Deliverables:
 - cleaned code (explain every change)
 - complexity before/after
 - regression tests
 - migration notes if behavior changes
  

4) Generate tests from spec

You are a test automation engineer.
Spec: <paste feature spec or acceptance criteria>
Deliverables:
 - unit tests (name, input, expected)
 - integration test scenarios
 - recommended test data and mocks
 - flaky test prevention tips
  

5) Create CI/CD pipeline step

You are a DevOps engineer.
Goal: Provide a CI/CD pipeline configuration for <GitHub Actions / GitLab CI / CircleCI / Jenkins>.
Context: Build > Test > Lint > Deploy to <staging/prod>.
Deliverables:
 - pipeline YAML with jobs and artifacts
 - secrets handling method
 - rollback strategy and health checks
  

6) Documentation & changelog

You are a technical writer.
Goal: Produce user-facing docs and a changelog for v<X.Y.Z>.
Deliverables:
 - short "what's new" summary
 - migration guide (if breaking changes)
 - updated README with usage, examples, and troubleshooting
 - doc table of contents
  

Parameters you should include (common options)

• Language — e.g., Python, TypeScript, Java, Go
• Framework — e.g., Django, FastAPI, NestJS
• Target environment — e.g., serverless, Kubernetes, on-prem
• Complexity — toy/prototype, production-ready, enterprise
• Security & compliance — crypto, OWASP, GDPR
• Testing — unit, integration, end-to-end
• Deliverable format — code files, step-by-step plan, diagrams

Practical examples (pick one and run)

Example A — "Add a create-user endpoint with validation and tests (FastAPI + Pydantic)":

Use the general wrapper.
Goal: Implement POST /users that creates a user with email + password (hashed).
Context: Python 3.11, FastAPI, PostgreSQL via SQLAlchemy.
Deliverables:
 - models.py (SQLAlchemy User)
 - api/users.py (endpoint + input validation)
 - tests/test_users.py (pytest, SQLite in-memory)
 - migration note and security: password hashing with bcrypt, validate email format.
  

Example B — "Generate GitHub Actions for monorepo CI":

Goal: Create a workflow that detects changed packages in a monorepo and runs tests only for affected packages.
Context: Node.js monorepo with Yarn workspaces.
Deliverables:
 - .github/workflows/ci.yml
 - script to compute affected packages
 - caching instructions
  

Tips for prompt-engineering success

• Start with the role (expert, senior, architect) — it frames the tone.
• Provide concrete constraints (versions, prohibited libraries, performance targets).
• Ask for explanations of trade-offs when design decisions are given.
• Request multiple options: “Give 3 alternative designs with pros/cons.”
• Use iterative prompts: produce a draft, then ask for tests, then ask for optimizations.

Short checklist before deploying AI-generated code

1. Security scan (SAST, dependency check)
2. Run unit & integration tests locally
3. Code review by a human
4. Verify licenses of suggested libraries
5. Monitor after deploy (logs, error budgets)

Closing notes

These templates are starting points — adapt the "Context" and "Constraints" fields to match your project. If you want, paste a short description of a real feature and I will produce a fully-filled prompt and the AI's expected output (code + tests + CI snippet) ready to paste into your assistant.