The LLM Debugging Playbook

Stop Wasting Weeks Debugging LLMs — a systematic method for diagnosing & fixing production AIYour LLM works in the demo. Then real traffic hits, and you lose a week tuning a prompt for a bug that was never in the prompt.Most LLM debugging is guessing dressed up as work. This book replaces the guessing with a method — a repeatable loop you run every time, the same way the best on-call engineers do. Reproduce the failure deterministically. Instrument before you theorize. Isolate which of the six layers actually owns the bug. Then fix the right thing, verify it honestly, and turn it into a test so it never comes back.Part I gives you the method. Part II is 13 symptom playbooks — when you're paged at 3am, jump to the one that matches what you're seeing and run the moves in order. Part III is 11 war stories: the cache that served another tenant's answer, the fine-tune that forgot how to count, the rate limit that disguised itself as latency. The wrong turns are left in on purpose. Part IV and the appendices hand you the harness, runbook, decision tree, and copy-paste code to make the next bug cheaper than the last.One idea runs through all of it: the model is a powerful, stochastic, untrusted component. Reliability lives in the boundaries you build around it — so you debug the boundaries first and the model last.No frameworks to buy. No theory to wade through. Just the discipline that separates teams who fix LLM failures in an afternoon from teams who lose weeks to them.Your LLM works in the demo. Then it breaks in production — and you lose a week.Here's the part nobody admits: most of that week is guessing. Tweak the prompt, rerun, hope. Add a retry, rerun, hope. The bug was never where you were looking.The engineers who fix these in an afternoon aren't smarter. They run a method:→ Reproduce it deterministically (a flaky bug is just one you haven't pinned)→ Instrument before you theorize (read the failure, don't reconstruct it)→ Isolate the layer — input, retrieval, prompt, model, parsing, or downstream→ Fix the right thing, verify honestly, and write the regression testSame loop, every time. The model is the last thing you suspect, not the first — it's a stochastic, untrusted component, and reliability lives in the boundaries around it.I wrote the whole method down: a 7-step loop, 13 symptom playbooks for when you're paged, and 11 war stories with the wrong turns left in.📘 The LLM Debugging Playbook — launching at 35% off.

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