AI Coding Pitfalls You Are Still Falling for in 2026 [#]
written on February 2nd, 2026 by Pascal Behmenburg
AI Can Still Feel Frustrating [#]
Imagine spending hours wrestling with a bug, only to finally hand it off to AI as a desperate Hail Mary, then watching it flop spectacularly. Sound familiar? In 2026, with tools like Cursor and Claude Code revolutionizing the way we write code, you’re not alone if AI sometimes feels like a frustrating sidekick rather than the 10x superpower you were promised.
The culprit? A handful of mistakes I see developers making repeatedly, even when using the best models available. In this post, I’ll break down these pitfalls and show you how to flip them into an unfair advantage.
Selecting the Right Problems [#]
There’s a common misconception spreading across tech Twitter: that AI can replace software engineers entirely. I push back on this idea constantly. At the end of the day, when code processes customer data and something goes wrong, the enterprise (or worse, you as the engineer who instructed the AI) takes the fall. You might accept this risk on hobby projects where only your own data is at stake, but in professional contexts, not reviewing AI-generated code is negligent.
Beyond liability, there’s a deeper problem. When you let AI generate code in a domain you don’t understand, whether that’s an unfamiliar language, framework, or problem space, you can’t assess its quality. You’ll accumulate technical debt at 10x the rate of a junior developer, but without any of the learning that usually accompanies early-career mistakes.
So how should you use AI? Treat it like a capable junior engineer who needs clear instructions. Start with problems you already know how to solve rather than using AI as a last resort after human attempts fail. This might seem counterintuitive, but there’s a method to it: by testing AI on familiar ground, you can validate its capabilities and understand its limitations before trusting it with unknowns.
Give the AI the exact context you’d provide when handing off a task to a junior. For debugging, include the logs and stack traces. Write out reproduction steps that reliably trigger the issue. Explain what’s actually wrong and why it matters. Define what “done” looks like with clear acceptance criteria. Tell it how to verify the solution and what formatting or linting tools the project uses.
Over time, build a collection of reproducible tests from bugs you’ve fixed yourself. Use these to benchmark how different models perform since AI capabilities evolve rapidly, and what failed three months ago might work today.
Context Management Pitfalls [#]
You’ve followed the advice above, crafted a detailed prompt, and gotten a decent result. Now you continue iterating in the same chat. This is where many developers hit a wall without understanding why.
AI agents operate within a context window, which is a fixed-size buffer that holds the conversation history. When you open Claude Code, you create a new context. Every message you send and every response the model generates gets stored there. But here’s the critical detail: the context window isn’t memory in the human sense. It’s reused as input for every subsequent request. The model doesn’t “remember” your conversation; it re-reads the entire context each time.
This window is limited to roughly 160-200k tokens, and it’s not all available to you. The system prompt, Claude Code’s instructions, any MCPs you’ve configured, and your CLAUDE.md file all consume tokens before you’ve typed a single word. Add in the code files Claude reads during implementation, and even a single bug fix can push you toward exhaustion.
Research shows that fuller context windows lead to worse outputs and more hallucinations. Models suffer from “lost in the middle” effects where details buried in long contexts get ignored. When you hit the limit, Claude summarizes the conversation to itself, compressing hours of nuanced discussion into a fraction of its original detail. Crucial information you provided earlier may vanish because the model deemed it unimportant.
The solution is simple: one problem per chat.
This maps directly to engineering best practices. A well-scoped issue becomes your prompt. Claude implements. You review and test. You might iterate a few times within the same session, asking for fixes or adjustments, but when the task is complete, you create a PR and close the chat.
If you can’t complete a task within a single context window, that’s a signal: either your problem was too large for one PR, or you’re polluting the context with unnecessary information. Both problems have the same remedy: scope down and stay focused.
Good Context Strategies [#]
I’ve seen developers reach for tools that dump entire codebases into context. Don’t do this. Excessive context causes “context rot” where models actually perform worse when overwhelmed with information due to the mechanics of next-token prediction.
Instead, favor less code and more markdown. Describe problems precisely in natural language. Specify what areas of the codebase shouldn’t be touched. Provide high-level navigation maps when the structure isn’t obvious. Let the AI search selectively using its tools rather than front-loading everything.
Tools like Claude Code succeed precisely because they pull only the files needed for each task, mimicking how a human would search a codebase. Trust the agent to find what it needs.
Keep an iterative CLAUDE.md file that steers the model away from repeated mistakes. If Claude keeps spinning up dev servers when you don’t want it to, add a note. If it consistently reaches for the wrong testing framework, correct that too. These small adjustments compound over time.
How to Set Up Claude Code for Success [#]
Enough theory. Let’s talk practical setup.
Start by asking yourself: what would you tell a new developer contributing to this codebase? How to format code, which dependencies to avoid, what commit conventions to follow, how to run tests. Write these rules down in a markdown file that the AI will read with every request.
For Cursor, use rule files with “always apply” enabled. For Claude Code, use the CLAUDE.md file in your project root. Keep it concise because every word costs tokens, and verbose explanations waste context that could be spent on your actual problem.
Example CLAUDE.md for a TypeScript React project:
# Project: dashboard-app ## Stack - React 18 + TypeScript 5 - Vite for builds - TailwindCSS for styling - React Query for server state - Vitest + React Testing Library for tests ## Commands - `pnpm dev` - Start dev server - `pnpm test` - Run tests - `pnpm lint` - ESLint + Prettier check - `pnpm typecheck` - TypeScript strict mode ## Conventions - Functional components only, no classes - Use `const` arrow functions for components - Colocate tests: `Component.tsx` → `Component.test.tsx` - Use conventional commits: feat|fix|docs|refactor|test - Prefer composition over prop drilling - All API calls through `/src/api/` modules ## Don't - Don't add new dependencies without discussion - Don't modify `/src/legacy/` (scheduled for removal) - Don't use `any` type (use `unknown` and narrow)
For MCPs, be selective. I use a single GitLab MCP at work that lets Claude fetch issues, create merge requests, and commit changes. This enables running multiple Claude instances across git worktrees, each handling separate tasks and creating PRs I’m assigned to. The merge request descriptions have been good enough that I’ve never had to edit them.
But remember: every MCP adds overhead to every request. A Sentry MCP might be useful for debugging workflows, but don’t load five MCPs “just in case.”
Crafting Effective Prompts [#]
With your environment configured, prompts can stay focused on the problem rather than repeating project conventions.
Bug fix prompt example:
## Bug: User avatar not loading on profile page ### Reproduction 1. Log in as any user with a custom avatar 2. Navigate to /profile 3. Avatar shows broken image icon ### Expected Avatar should display the uploaded image ### Logs ``` GET /api/users/123/avatar 403 Forbidden X-Request-ID: abc-123 ``` ### Context - Avatar upload works correctly (verified in S3) - Started after deploy #847 (auth refactor) - Other authenticated endpoints work fine ### Acceptance - Avatar loads for logged-in users viewing their own profile - Avatar loads for users viewing others' profiles - Add test covering this scenario
Feature implementation prompt example:
## Feature: Export dashboard data to CSV ### User Story As a dashboard user, I want to export the current view's data to CSV so I can analyze it in spreadsheet software. ### Acceptance Criteria - [ ] "Export CSV" button appears in dashboard header - [ ] Clicking exports currently visible data (respects active filters) - [ ] Filename includes dashboard name and current date - [ ] Large exports (>10k rows) show progress indicator - [ ] Works across all dashboard types: analytics, sales, inventory ### Technical Notes - Use existing `useExport` hook pattern from /src/hooks/ - CSV generation should happen client-side for small datasets - Consider streaming for large exports ### Out of Scope - PDF export (separate ticket) - Scheduled exports - Custom column selection
Stay Current [#]
AI capabilities evolve fast. What failed three months ago might work flawlessly today. Outdated experiences skew your expectations and cause you to miss breakthroughs. Stay plugged into communities, follow changelogs, and periodically retest assumptions.
The techniques in this post reflect what works in early 2026, but the landscape shifts constantly. The engineers who thrive are those who treat AI as a rapidly evolving tool rather than a static utility.
Closing Thoughts [#]
Working effectively with AI isn’t about surrendering agency to the machine. It’s about understanding its constraints and leveraging its strengths. The model is only as capable as the person instructing it and evaluating the results.
We still need engineers for the mental models, for understanding problem spaces deeply enough to guide solutions, and for the critical review that separates working code from correct code. What’s changed is that writing boilerplate, debugging rote issues, and scaffolding implementations no longer need to consume your time.
Don’t let this discourage you from learning. The better you understand your domain, the better you can direct the AI and catch its mistakes. These tools amplify capability, but there has to be something to amplify.