Want to level up your debugging with LLM copilots?
Give your logs structure. Give them context. Make them readable.
And yes — make them beautiful too.
🚫🐛 04.31 [engine.py:start_motor] Voltage too low
That one line might save you hours.
I learned a very valuable lesson working with large language models (LLMs) like Gemini (and honestly, ChatGPT too): clear, consistent, and machine-readable debug messages can massively speed up troubleshooting — especially on complex, multi-file projects.
It’s something I used to do occasionally… but when I leaned into it fully while building a large system, the speed and accuracy of LLM-assisted debugging improved tenfold. Here’s the trick:
This tiny statement prints:
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A visual marker (🚫🐛) so debug logs stand out,
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A timestamp (MM.SS) to see how things flow in time,
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The file name and function name where the debug is triggered,
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And finally, the actual message.
All this context gives the LLM words it can understand. It’s no longer guessing what went wrong — it can “see” the chain of events in your logs like a human would.
Why It Works So Well with LLMs
LLMs thrive on language. When you embed precise context in your debug prints, the model can:
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Track logic across files,
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Understand where and when things fail,
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Spot async/flow issues you missed,
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Suggest exact fixes — not guesses.