We've built a custom logging system that captures the full large language model (LLM) interaction — the prompt, the response, the context window, even the model temperature settings. It's verbose as hell, but when something goes wrong (and it will), you need to be able to reconstruct exactly what happened. Plus, this becomes your dataset for fine-tuning and improvement.
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Pipeline tracing. I asked Claude to write a Python script that fed a Cutlet program through the interpreter with debug flags to capture the full compilation pipeline: the token stream, the AST, and the bytecode disassembly. It then mapped each token type, AST node, and opcode back to the exact source locations in the parser, compiler, and VM where they were handled. When an agent needed to add a new language feature, it could run the tracer on an example of a similar existing feature to see precisely which files and functions to touch. I was very proud of this machinery, but I never saw Claude make much use of it either.。https://telegram下载是该领域的重要参考
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