AI for Developers

For reasoning-heavy tasks like code review, architecture decisions, and multi-file debugging, Claude outperforms ChatGPT due to its larger context window and stronger reasoning. For rapid code generation, API lookup, and quick script writing, both perform similarly. For inline IDE autocomplete, GitHub Copilot and Cursor remain the better tool regardless of which chat model you prefer.

AI code review catches what human reviewers deprioritize under time pressure: naming inconsistencies, missing edge case handling, and documentation gaps. It misses what requires business context: whether an abstraction fits the existing architecture, whether an API contract matches how other services use it. The right workflow is AI review first to catch surface issues, then human review focused on judgment-level concerns.

The key is context, not just the error message. Paste the exact error, the relevant code, the library versions including patch versions, and what you have already tried. Library version mismatches are one of the most common reasons AI debugging help produces wrong answers. If you are on React Query v5 and the AI is reasoning about v4 behavior, every suggestion about query invalidation will be incorrect. State your versions explicitly.

Yes, but only if you give it more than the code. AI-generated documentation from code alone is accurate but useless because it describes what without why. Feed it the code, the decision context, and the use case it was built for. The output will be documentation that answers the question engineers actually ask: why does this exist and when should I use it instead of the alternative.

Specify constraints first. Generic system design prompts produce textbook answers. Useful responses come from prompts that include your current stack, approximate scale, team size, and the specific tradeoff you are most concerned about. "Design a caching layer" produces a lecture. "Design a caching layer for a Next.js app with 50k daily active users where cache invalidation on user-generated content is the primary challenge" produces a usable recommendation.

Claude is particularly useful for TypeScript type inference problems, complex generic constraints, and strict mode migrations such as enabling noUncheckedIndexedAccess or exactOptionalPropertyTypes. It understands the difference between widening and narrowing and can explain why a type error occurs in a way that compiler messages alone cannot. For tsconfig changes, ask it to explain the downstream effects on existing code before applying them.

AI is best at generating test scaffolding and edge case lists, not at writing the assertions that verify business logic. Ask it to list every edge case for a function, write test structure without assertions, or review an existing test file for coverage gaps. Be cautious about letting AI write the test for a function it also wrote. The risk is that the test validates the implementation rather than the requirement.