Musembi · Oct 26, 2026 · 11:51 AM UTC

Musembi

𝐇𝐎𝐖 𝐈 𝐔𝐒𝐄 𝐂𝐋𝐀𝐔𝐃𝐄 𝐂𝐎𝐃𝐄 𝐓𝐎 𝐁𝐔𝐈𝐋𝐃 𝐑𝐄𝐋𝐈𝐀𝐁𝐋𝐄 𝐒𝐎𝐅𝐓𝐖𝐀𝐑𝐄, 𝐍𝐎𝐓 𝐉𝐔𝐒𝐓 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐄 𝐂𝐎𝐃𝐄 AI coding tools have changed how we build software. But there’s one thing I’ve learned: generating code is easy. Building software that actually works in production is the hard part. Claude Code can help you build features, debug errors, refactor existing code, write tests, and understand unfamiliar codebases. But the quality of the final product depends heavily on how you use it. Here’s the workflow I would follow when building a real SaaS product. 1. PLAN BEFORE YOU CODE Don't start by asking AI to build your entire application. Define the problem, architecture, database schema, user roles, API endpoints, and expected behaviour first. A clear plan reduces unnecessary changes and prevents the AI from making architectural decisions based on incomplete context. 2. BUILD IN SMALL, TESTABLE STEPS Instead of saying, "Build me an entire SaaS platform," break the project into manageable tasks. Start with authentication. Then implement permissions, database operations, core business logic, and the user interface. Test each component before moving to the next. Smaller changes are easier to review, debug, and maintain. 3. NEVER TRUST GENERATED CODE WITHOUT TESTING IT AI-generated code can look correct while containing security vulnerabilities, edge cases, or incorrect assumptions. Run tests. Inspect the implementation. Validate inputs. Check error handling. Test what happens when requests fail. For a SaaS application, pay special attention to authentication, authorization, data isolation, and payment processing. 4. MAKE YOUR AI UNDERSTAND THE CODEBASE Give Claude Code clear project instructions, coding conventions, architectural decisions, and testing requirements. Keep documentation updated and explain important constraints. The more accurately the tool understands your project, the less time you spend correcting inconsistent implementations. 5. USE GIT AS YOUR SAFETY NET Commit working code regularly. Review diffs before accepting major changes. Keep changes focused. Use branches when experimenting with risky implementations. If an AI-generated change breaks something, you should be able to identify the problem and recover without losing working code. 6. THINK ABOUT PRODUCTION FROM DAY ONE A feature working on your laptop doesn't mean it's ready for real users. Consider database migrations, environment variables, logging, monitoring, rate limiting, backups, deployment, and rollback strategies. Security and reliability should be part of development, not tasks postponed until launch. THE BIGGEST LESSON Claude Code can dramatically accelerate software development, but speed without engineering discipline can create technical debt faster than ever. The developers who benefit most from AI won't necessarily be those who generate the most code. They'll be those who can define the right problems, review implementations critically, test thoroughly, and turn generated code into dependable software.