Prompting Claude for Code Generation

Claude is an exceptionally capable coding assistant, but the quality of the code you get depends heavily on how you prompt. Generic requests produce generic code. Precise, contextual prompts produce production-quality output. This lesson covers the patterns that consistently yield the best results across different languages and tasks.

Language-Specific Context Matters

Claude adjusts its idioms, patterns, and conventions based on language context. Providing language-specific expectations upfront dramatically improves output quality.

Python — Specify whether you want type hints, docstrings, dataclass vs dict, sync vs async, and which Python version. Claude defaults to broadly compatible patterns unless told otherwise.

TypeScript — Clarify strictness expectations: strict null checks, explicit return types, utility types vs interfaces, whether to use zod for runtime validation. Ask for satisfies over as when you want type safety.

Rust — State your error handling approach upfront: Result/? operator, thiserror, anyhow, or color-eyre. Mention whether you want idiomatic lifetimes or a simpler clone-heavy approach for prototyping.

Go — Indicate whether to follow stdlib patterns, whether to use context.Context propagation, and your preferred error-wrapping style (fmt.Errorf with %w, errors.New). Go idioms are distinct and Claude respects them when you signal them.

Test-Driven Prompting: Write Tests First

The single most effective technique for code generation is providing tests before asking for an implementation. This is true test-driven development applied to prompting.

When you give Claude a test file, it knows:

• The exact function signatures expected
• The expected input/output behavior
• Edge cases you care about
• The testing framework in use

Without tests, Claude infers intent from your description and may infer wrong. With tests, Claude has a deterministic specification to implement against.

Debugging Prompts: Give Claude the Full Picture

When asking Claude to fix a bug, the quality of the fix correlates directly with how much context you provide. The minimum viable debugging prompt includes:

1. The error message (exact text, not a paraphrase)
2. The stack trace (full, not truncated)
3. The relevant code (the function or module where the error occurs)
4. What you expected to happen vs what actually happened
5. Any recent changes that preceded the error

Omitting any of these forces Claude to guess. Provide all five and Claude can diagnose and fix in one pass.

<error>
  TypeError: Cannot read properties of undefined (reading 'map')
  at CourseList (CourseList.tsx:34:22)
  at renderWithHooks (react-dom.development.js:14985:18)
</error>

<code>
  [the CourseList component]
</code>

<context>
  This error occurs only after navigating back from a lesson page.
  On initial render the component works correctly.
  I recently added a useEffect that fetches courses on mount.
</context>

<instructions>
  Diagnose why courses is undefined on back-navigation and provide a fix.
  Explain the root cause before showing the fix.
</instructions>

The Scaffold → Implement → Test → Refactor Pattern

For non-trivial features, break the work into four prompts rather than one. This produces cleaner, more reviewable code at each stage.

Step 1 — Scaffold: Ask for the file structure, type definitions, and empty function signatures. Review and confirm the architecture before any implementation.

Step 2 — Implement: Ask Claude to fill in one function or module at a time. Smaller units are easier to verify and easier for Claude to get right.

Step 3 — Test: Ask for tests for the implementation you just reviewed. Since you've already seen the code, the tests can be more targeted.

Step 4 — Refactor: Once you have passing tests, ask Claude to improve readability, performance, or error handling without changing behavior.

This pattern works especially well for features that touch multiple files, as it prevents Claude from making large-scale architectural decisions that are hard to undo.

Prompt Patterns by Task Type

New function:

<context>[language, project type, imports available]</context>
<instructions>
  Write a function that [exact behavior].
  Signature: [function signature]
  Constraints: [performance, type safety, error handling approach]
</instructions>

Refactor existing code:

<instructions>Refactor the following code to [specific goal: extract logic, reduce nesting, improve readability]. Do not change behavior — existing tests must still pass.</instructions>
<code>[the code to refactor]</code>

Migrate to new API:

<context>Migrating from [old library/version] to [new library/version]</context>
<migration_guide>[relevant changelog or docs excerpt]</migration_guide>
<code>[code to migrate]</code>
<instructions>Update this code to use the new API. Flag any patterns that have no direct equivalent.</instructions>

Fix a bug:

<error>[error message + stack trace]</error>
<code>[relevant code]</code>
<context>[what changed recently, what behavior is expected]</context>
<instructions>Fix the bug. Explain the root cause first, then provide the corrected code.</instructions>

Exercise: Write a TDD-Style Prompt

Key Takeaways

• Provide language-specific context upfront: version, conventions, libraries in use, and strictness expectations
• Test-driven prompting is the most reliable technique for getting correct implementations — write tests first, then ask for the implementation
• For debugging, always include the full error message, full stack trace, the relevant code, and what changed recently
• Break large features into Scaffold → Implement → Test → Refactor to maintain reviewability at each stage
• Use XML tags to separate test code, error messages, existing code, and instructions — this gives Claude clear structural cues for each component