14.3 Backend and API Prompts (2)

{
"code": "return jsonify({\"message\": \"Book added successfully\"}), 201\n\n@app.route(\"/books\", methods=[\"GET\"])\ndef get_books():\n    books = Book.query.all()\n    return jsonify([{\"id\": b.id, \"title\": b.title, \"author\": b.author} for b in books])\n\n@app.route(\"/books/<int:book_id>\", methods=[\"GET\"])\ndef get_book(book_id):\n    book = Book.query.get(book_id)\n    if not book:\n        return jsonify({\"error\": \"Book not found\"}), 404\n    return jsonify({\"id\": book.id, \"title\": book.title, \"author\": book.author})\n\n@app.route(\"/books/<int:book_id>\", methods=[\"DELETE\"])\ndef delete_book(book_id):\n    book = Book.query.get(book_id)\n    if not book:\n        return jsonify({\"error\": \"Book not found\"}), 404\n    db.session.delete(book)\n    db.session.commit()\n    return jsonify({\"message\": \"Book deleted\"})\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n\n## 14.4 Testing and Debugging Prompts\nTesting and debugging are where Claude Code transitions from being a coding assistant to acting as a software quality partner. Well-written prompts can help Claude not only generate test cases but also diagnose logic errors, interpret stack traces, and even explain how to fix bugs. This section focuses on writing structured, reproducible testing and debugging prompts that yield high-quality, verifiable results.\n\nClaude’s advantage lies in its ability to reason contextually — to understand what a function should do, detect why it fails, and produce both tests and fixes in a single workflow. With the right prompt structure, you can treat Claude as a flexible test engineer that complements your own intuition and saves hours of manual inspection.\n\n### Concept Development\nWhen prompting Claude for testing and debugging, clarity and containment are critical. Unlike human testers, Claude does not infer unstated requirements well — you must specify scope, framework, and expected behavior explicitly. Key prompt elements that consistently yield strong results include:\n\n- Framework specification– e.g., “Use pytest,” “Use Jest,” or “Use unittest.”\n- Coverage scope– Define which functions, modules, or edge cases to test.\n- Expected format– Ask for runnable test files, not snippets.\n- Error handling– Provide sample stack traces for Claude to analyze.\n- Verification intent– Instruct Claude to confirm that the fix passes all tests.\n\nA well-structured debugging prompt doesn’t just request a fix; it explains the observed behavior and invites Claude to reason through it step-by-step.\n\n### Hands-On Example 1: Writing Unit Tests with Pytest\n**Prompt**:\nYou are a Python QA engineer.\nWrite pytest test cases for the following function.\nEnsure coverage for valid input, invalid input, and edge cases.\nReturn a complete runnable test file.\n\n```python\ndef divide(a, b):\n    return a / b\n```\n\n**Claude Output (Example)**:\n```python\nimport pytest\nfrom main import divide\n\ndef test_divide_valid():\n    assert divide(10, 2) == 5\n    assert divide(-6, 3) == -2\n\ndef test_divide_zero_division():\n    with pytest.raises(ZeroDivisionError):\n        divide(5, 0)\n\ndef test_divide_floats():\n    assert pytest.approx(divide(5.0, 2.0), 0.001) == 2.5\n\ndef test_divide_large_numbers():\n    assert divide(1_000_000, 10) == 100_000\n```\n\n**Explanation**: Claude created a clean, structured test file with edge coverage — including floating-point precision and error handling. It automatically inferred boundary conditions without being told exactly what to test, thanks to a well-framed prompt.\n\n### Hands-On Example 2: Debugging with Error Trace Analysis\n**Prompt**:\nYou are a debugging assistant.\nAnalyze the following Python traceback and identify the root cause.\nThen, rewrite the buggy code correctly with an explanation.\n\n```\nTraceback (most recent call last):\n  File \"app.py\", line 10, in <module>\n    result = calculate_area(5)\nTypeError: calculate_area() missing 1 required positional argument: 'height'\n\n```python\ndef calculate_area(width, height=2):\n    return width * height\n```\n\n**Claude Output (Example)**:\n```python\n# Fixed Code\ndef calculate_area(width, height=2):\n    return width * height\n\n# Correct Usage\nresult = calculate_area(5, 4)\nprint(result) # Output: 20\n```\n\n**Explanation**: Claude correctly identifies the issue — the function call is missing a required positional argument. It not only provides the corrected code but also explains the fix in context, turning debugging into a learning process rather than a guessing game.\n\n### Hands-On Example 3: JavaScript Jest Test Automation\n**Prompt:**\nWrite Jest test cases for the following Express.js route.\nCover success, failure, and validation scenarios.\n\n```javascript\napp.post(\"/users\", (req, res) => {\n    const { name, email } = req.body;\n    if (!name || !email) return res.status(400).json({ message: \"Invalid input\" });\n    res.status(201).json({ message: \"User created\" });\n});\n```\n\n**Claude Output (Example):**\n```javascript\nimport request from \"supertest\";\nimport express from \"express\";\n\nconst app = express();\napp.use(express.json());\n\napp.post(\"/users\", (req, res) => {\n    const { name, email } = req.body;\n    if (!name || !email) return res.status(400).json({ message: \"Invalid input\" });\n```"
}

res.status(201).json({ message: "User created" });

});

describe("POST /users", () => {

it("should create a user successfully", async () => {

const res = await request(app)

.post("/users")

.send({ name: "Alice", email: "alice@example.com" });

expect(res.statusCode).toBe(201);

expect(res.body.message).toBe("User created");

});

it("should return 400 for invalid input", async () => {

const res = await request(app).post("/users").send({});

expect(res.statusCode).toBe(400);

});

});

Why It Works: The prompt defines three expectations — framework, test coverage, and validation behavior. Claude responds with a working test suite that uses Supertest, handles both valid and invalid inputs, and runs immediately in Node.js.

Hands-On Example 4: Debugging Logic Errors in a Loop

Prompt:

Analyze this Python code that produces incorrect results.

Explain the logical bug and provide the corrected version.

def calculate_sum(numbers):

total = 0

for num in numbers:

total = num # bug here

return total

print(calculate_sum([1, 2, 3]))

Claude Output (Example):