Code Generation Prompts: Writing Modular Functions, Unit Tests & APIs (2026)

AI-assisted software engineering relies on structured Code Generation Prompts. By providing explicit technical stack requirements, error-handling rules, and output schemas, developers transform LLMs into high-speed coding assistants capable of generating clean microservices and complete unit test suites.

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1. Master Code Generation System Instruction

```xml

Senior Backend Engineer (Python 3.11 / FastAPI)

- Framework: FastAPI with Pydantic v2 validation

- Database ORM: SQLAlchemy 2.0 async session

- Async HTTP: `httpx` with exponential backoff

1. Include strict type annotations (`def func(param: str) -> dict:`).

2. Handle exceptions defensively using FastAPI `HTTPException`.

3. Return self-contained executable code inside ```python code blocks.

```

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2. Automated Unit Test Generation Prompt

```xml

Write a comprehensive PyTest unit test suite for the provided Python function.

- Include tests for valid inputs, boundary conditions (null/empty), and unexpected exceptions.

- Use `@pytest.mark.parametrize` for repetitive input grids.

- Mock external network calls using `unittest.mock.patch`.

```

To manage and version-control your team's code generation prompts, deploy them on PromptOptima.

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Frequently Asked Questions

Why do simple 'write code for X' prompts produce buggy implementations?

Simple prompts omit architectural boundaries, language version specifics, error handling rules, and type annotations, forcing the model to infer defaults.

How can I prompt an LLM to generate 100% path-covered unit tests?

Provide the source code inside `` tags and instruct the model to enumerate happy path, boundary edge-cases, and exception handling tests separately.

What is the best way to prompt for microservice API endpoints?

Specify framework versions (e.g. Express, FastAPI), input validation libraries (Zod, Pydantic), and database ORM specifications explicitly in the system instructions.