Code Complete

Can AI tools review my code for bugs and quality?

Yes — several tools specialize in AI code review. GitHub Copilot has PR review built in, CodeRabbit offers automated review on every PR, and Claude Code can analyze entire codebases. They catch logic errors, suggest optimizations, flag security issues, and enforce style consistency. Most effective as a complement to human review, not a replacement — AI catches different types of issues than humans do.

How do large language models actually generate code?

LLMs generate code by predicting the most likely next token based on patterns learned from billions of lines of code during training. They don't "understand" code the way humans do — they recognize statistical relationships between tokens. When you provide a prompt, the model samples from a probability distribution to produce output that statistically resembles correct code for your context.

Why does AI hallucinate code, and how do I avoid it?

AI hallucination in code happens because models predict plausible-sounding tokens rather than verifying factual accuracy. They may invent API methods that don't exist, reference deprecated packages, or confidently produce broken logic. To minimize hallucinations: provide explicit context, ask the model to cite its sources, test all generated code, and use tools with retrieval-augmented generation (RAG) over real documentation.

What is Deepseek Coder and how does it compare to CodeLlama?

Deepseek Coder is a family of open-source code-focused LLMs from DeepSeek AI that outperforms CodeLlama on most coding benchmarks, especially for Python, JavaScript, and C++. Deepseek Coder v2 (236B MoE) rivals GPT-4 in many code tasks. CodeLlama from Meta was groundbreaking when released but Deepseek Coder has largely surpassed it. For local use via Ollama, Deepseek Coder 6.7B or 33B are the most practical sizes.