How to Code with AI Without Losing Control
AI coding assistants accelerate development dramatically. At Anthropic, 90% of Claude Code’s codebase comes from Claude itself. Yet using LLMs effectively requires discipline—they amplify your skills but demand constant oversight.
Plan Before You Code
Write specifications before generating code. Start by describing your project to the LLM and ask it to question you until requirements crystallize. Document everything in a spec.md covering requirements, architecture, data models, and testing strategy.
Next, generate a project plan. Feed your spec to a reasoning model and ask it to break implementation into discrete tasks. Iterate on this plan until it’s complete. This “waterfall in 15 minutes” prevents wasted effort later.
Only after planning do you start coding. Both you and the LLM now understand exactly what you’re building.
Work in Small Chunks
Break projects into single-feature tasks. Implement one function, fix one bug, add one feature at a time. LLMs excel at focused prompts but struggle with large, monolithic requests.
After planning, prompt: “Let’s implement Step 1 from the plan.” Code it, test it, then move to Step 2. This prevents the inconsistent mess developers report when asking for too much at once—duplicate logic, mismatched names, no coherent architecture.
Generate a structured prompt plan file containing task sequences. Tools like Cursor execute them one by one. Iterate in small loops to catch errors early and course-correct quickly.
Provide Complete Context
Feed the AI everything it needs: relevant code, technical constraints, API documentation, and known pitfalls. Tools like Claude Projects can import entire repositories. Use context tools like Context7 or manually include key files.
Do a “brain dump” before coding: high-level goals, working examples, approaches to avoid. If using niche libraries, paste official docs so the AI doesn’t guess. Tools like gitingest or repo2txt bundle codebases into text files for LLM ingestion.
Guide with inline comments: “Here’s the current X implementation. Extend it to do Y without breaking Z.” LLMs follow detailed instructions—provide them.
Choose Models Strategically
Different models have different strengths. If one model struggles, try another. Copy the same prompt to different services to compare approaches. Use the newest “pro” tier models when quality matters.
Try multiple models in parallel to cross-check solutions. Don’t hesitate to switch models mid-project—sometimes a second opinion clarifies the path forward.
Use Specialized AI Tools
Command-line tools like Claude Code, OpenAI’s Codex CLI, and Google’s Gemini CLI work directly in project directories—reading files, running tests, fixing issues. Asynchronous agents like Jules and GitHub Copilot Agent clone repositories, work on tasks, and open pull requests.
These tools aren’t infallible. Supply them with plans and context from earlier steps. Keep them on track by loading spec.md before execution. Monitor each step rather than letting agents work unattended.
Test Everything
Treat LLM output like code from a junior developer. Read it, run it, test it. Never blindly trust AI-generated code—it produces plausible-looking bugs with complete confidence.
Generate test plans during planning. Instruct tools to run test suites after implementing tasks and debug failures. Strong testing practices amplify AI usefulness. Without tests, agents assume everything works when it’s broken.
Use Chrome DevTools MCP to grant AI tools browser access for debugging. They inspect DOM, get performance traces, console logs, and network data—diagnosing bugs with runtime precision.
Spawn second AI sessions to review code from the first. Ask different models to critique implementations. AI-written code needs extra scrutiny—it convincingly hides flaws humans miss.
Commit Frequently
Make granular commits after each small task. If the AI’s next suggestion breaks something, revert to your last stable checkpoint. Treat commits as save points in a game.
Small commits with clear messages document development and simplify debugging. If five AI changes break something, separate commits reveal which one caused the issue. One giant “AI changes” commit makes this impossible.
Use branches or worktrees to isolate experiments. Spin up fresh worktrees for features—run parallel AI sessions without interference. Failed experiments disappear. Successful ones merge in.
Customize AI Behavior
Create rules files (CLAUDE.md, GEMINI.md) containing process rules, style preferences, and coding standards. Feed these at session start to align the model with your conventions.
Configure global instructions in tools like GitHub Copilot and Cursor. Specify indent style, naming conventions, linting rules. The AI’s suggestions will match team idioms.
Provide inline examples of desired output. Show similar existing functions: “Here’s how we implemented X, use this approach for Y.” LLMs excel at mimicry—prime them with patterns to follow.
Add “no hallucination” clauses: “If context is missing, ask for clarification rather than fabricating answers.” Instruct the AI to explain reasoning in comments when fixing bugs.
Leverage Automation
Run CI/CD, linters, and code review bots on every commit. Let AI trigger these and evaluate results. Feed failure logs back: “Integration tests failed with XYZ, let’s debug.”
Include linter output in prompts. If AI code fails linting, copy errors and say “address these issues.” Once aware of tool output, the model corrects mistakes.
Configure agents to refuse marking tasks “done” until tests pass. Treat code review bot feedback as improvement prompts. Combining AI with automation creates virtuous cycles—AI writes, automation catches issues, AI fixes them.
Stay Accountable
You remain the responsible engineer. Merge or ship code only after understanding it. If AI generates convoluted solutions, ask for explanations or rewrite in simpler terms. Dig into anything that feels wrong.
The LLM is your assistant, not an autonomous coder. You’re the senior developer; it accelerates you without replacing judgment. This protects code quality and your skill growth.
Continue coding without AI periodically to keep raw skills sharp. The developer-AI duo exceeds either alone, but the developer must uphold their end.
Keep Learning
Every AI coding session teaches you something. LLMs expose you to new languages, frameworks, and techniques. They amplify productivity if you bring solid engineering fundamentals but amplify confusion without that foundation.
The AI operates at higher abstraction levels—you focus on design, interface, architecture while it generates boilerplate. This requires having those high-level skills first. Using AI pushes you toward more rigorous planning and conscious architecture.
Review AI code to learn new idioms. Debug AI mistakes to deepen domain understanding. Ask the AI to explain its code—constantly interview it about decisions. Use it as a research assistant to compare options and trade-offs.
Next Steps
Apply classic software engineering discipline to AI collaborations. Design before coding, write tests, use version control, maintain standards. These practices become even more critical when AI writes half your code.
Start your next project with a detailed spec. Break work into small tasks. Provide complete context. Test relentlessly. Commit frequently. The AI accelerates mechanical parts while you guide direction and ensure quality.
Human engineers remain directors of the show—AI coding assistants are incredible force multipliers under expert guidance.