AI Code Explainer

Using an LLM to Read Code You Didn't Write

The fastest way to understand unfamiliar code used to be: read the docs, read the tests, then trace through the implementation. LLMs collapse that into "paste it in and ask." That works, but only if you know which questions to ask.

What a Good Explanation Includes

The tool's prompt is structured to extract four kinds of insight:

1. High-level intent. What's the function trying to accomplish, in one sentence?
2. Algorithm/pattern recognition. Is this a quicksort, a sliding window, a producer-consumer, a fan-out/fan-in? Naming the pattern unlocks the rest.
3. Line-by-line walkthrough. Especially for the non-obvious bits, bitmasks, regex, recursion bases, lock acquisition order.
4. Complexity. Best/average/worst case for time, plus space. Useful as a sanity check, not a final answer.

Why Complexity Estimates Are Often Wrong (Or Misleading)

LLMs reliably identify the dominant operation in simple code. They struggle with:

• Amortized costs. A std::vector::push_back is O(1) amortized but O(n) worst-case. Tools often report worst-case.
• Hidden costs. Calling .includes() on an array is O(n) per call. A loop with an includes check is O(n²), but the snippet visually looks O(n).
• Recursion with memoization. The tool sometimes counts the tree without the memo and reports exponential when it's polynomial.
• GC, allocator behavior, cache effects. These never show up.

Use the reported complexity to confirm your gut, not to replace measurement.

When the Explainer Is Most Useful

• Reading legacy code in an unfamiliar language. You can't git blame your way to understanding a 2000-line Perl module fast, but the explainer can summarize each subroutine.
• Reviewing PRs in a stack you don't own. "I'm reviewing a Rust PR but I'm a Python person", the explainer translates the idiomatic Rust into language-agnostic intent.
• Onboarding to a new codebase. Walk through unfamiliar files top-down: explainer for orientation, then dive into the parts that matter.
• Decoding clever one-liners. Bit twiddling, regex, functional combinator chains. The "what does this even do" moments.

When It Falls Down

• Code with non-obvious dependencies on context outside the snippet. Global state, framework conventions, build-time macros.
• Very long files. Token limits truncate; the explainer might focus on the first half and ignore the rest.
• Domain-specific code where the algorithm is non-public (proprietary trading strategies, ranking heuristics).
• Code that looks correct but is wrong. The explainer will confidently describe the buggy behavior as the intended one.

Privacy Note

Your snippet is sent to an LLM API for inference. Do not paste production secrets, API keys, internal algorithms you can't share, or PII. For sensitive code, the same prompt template can be run against a local model (Ollama with codellama, llama.cpp with a quantized 7B model) on hardware you control.