Stop Burning Tokens: How to Avoid Feeding LLMs Broken Code (CyberDudeBivash Guide)
Executive summary If you feed buggy code to an LLM you’ll get back buggy suggestions — and you’ll pay for them. The secret: fix as much as possible locally first , then send the smallest, most precise context the LLM needs. This guide gives a practical system you can adopt now: Local preflight: lint, unit tests, minimal reproducible example (MRE) generator. Prompt hygiene: diff-only prompts, test-driven prompts, and strict output formats. CI gating: only call LLM from CI when pre-checks pass or when a focused, failing-test payload is published. Token-aware engineering: estimate tokens, calculate cost, and budget. Developer tooling & templates: pre-commit hooks, Python/Node scripts, GitHub Actions examples. Follow this and you’ll cut wasted tokens, shorten review cycles, and produce higher-quality LLM outputs. Why engineers waste tokens Common anti-patterns: Dumping entire repositories into the prompt. Asking for “fix my code” without f...