Input model

class FactorialInput(BaseModel):

number: int

def calculate_factorial(n: int) -> int:

"""Compute factorial recursively with validation."""

if n < 0:

    raise ValueError("Number must be non-negative")

if n in (0, 1):

    return 1

return n * calculate_factorial(n - 1)

@app.post("/factorial")

async def factorial_endpoint(input_data: FactorialInput):

"""API endpoint to compute factorials safely."""

try:

    result = calculate_factorial(input_data.number)

    return {"input": input_data.number, "factorial": result}

except ValueError as e:

    raise HTTPException(status_code=400, detail=str(e))

except Exception:

    raise HTTPException(status_code=500, detail="Unexpected error occurred")

This example is complete and fully runnable with FastAPI. It includes validation, exception handling, and explanatory comments. The quality of the code directly stems from Claude’s step-by-step reasoning before writing the implementation.

If you run this API locally using `uvicorn main:app --reload` and send a JSON request such as `{"number": 5}`, the endpoint will return `{"input": 5, "factorial": 120}`.

Clarification Table

| Prompt Element | Purpose | Example Instruction | Effect on Output |
| --- | --- | --- | --- |
| Reasoning Phase | Encourages Claude to think before coding | “Outline your steps first.” | Produces structured and documented code |
| Implementation Phase | Separates logic from execution | “Then write the complete implementation.” | Generates cleaner and more reliable output |
| Validation Step | Forces Claude to check assumptions | “Include input validation and error handling.” | Prevents logical and runtime errors |
| Explanation | Reinforces comprehension | “Add comments explaining each section.” | Improves readability and maintainability |

This framework ensures every generated solution is deliberate and technically sound. Instead of treating Claude like an instruction follower, you’re training it to reason like a teammate.

Step-by-step and chain-of-thought prompting is how you make Claude Code think before it acts. It strengthens logical flow, reduces guesswork, and increases reliability across projects. By asking Claude to reason first, implement next, and validate at the end, you transform it into a deliberate engineering collaborator rather than an automated generator.

The next section builds on this concept by teaching you how to handle long contexts and multi-file prompts, so Claude can reason across entire projects and maintain coherence in large-scale applications.

## 3.3 Handling Long Contexts and Multi-File Prompts
As your projects grow, so does the number of files, functions, and dependencies that Claude Code must understand. Small snippets are easy for any coding assistant to handle, but real-world development rarely fits inside a few hundred lines. This is where Claude Code’s long-context capability becomes a true advantage. With support for extremely large token windows, Claude can read, understand, and reason across entire projects — multiple files, documentation, and configurations — all within a single session. When prompted correctly, Claude behaves like a developer who has read your entire repository and can give detailed, consistent feedback across every module. Learning to use long contexts effectively allows you to manage complex codebases with precision and maintain full architectural awareness throughout your interaction.

Concept Development

A context window defines how much information Claude can “see” at once — including your prompt, previous exchanges, and the code it’s analyzing. The larger the window, the more context Claude can retain when reasoning. In models like Claude 3 Opus, this extends to hundreds of thousands of tokens, meaning you can include several files, test data, and system documentation in one conversation.

However, providing Claude with too much unstructured data can dilute its focus. Like any experienced engineer, Claude needs a guided briefing to know which files are important and what to look for. Effective long-context prompting is about focus, hierarchy, and relevance. Instead of dumping code, you structure the context in a way that mirrors human reasoning — from the foundation (models and utilities) to higher layers (controllers, routes, and APIs).

When working with multi-file prompts, your goal is to help Claude retain relationships between files. You can do this by marking file boundaries clearly, summarizing intent before each file, and ending with a specific request. Think of yourself as a technical lead handing an assistant a stack of code — you wouldn’t say “read this,” you’d say “start with models.py, then see how it’s used in main.py, and tell me how they interact.” That same discipline applies to Claude.

Hands-On Example