When the stack trace becomes a suspect
I ran into a situation where an LLM-based assistant consistently pointed at the wrong root cause in production crashes. The model would parse the Java stack trace I pasted and highlight a familiar library call as the offender. On the surface the summaries were plausible: concise, confident, and written in a way that read like an experienced engineer. I pasted traces into a general AI workflow tool like crompt.ai when I wanted quick context and it gave me tidy narratives that felt actionable.
That confidence is exactly the problem. The assistant favored frames that looked familiar and omitted subtle chained exceptions and suppressed causes. It stripped file paths and line numbers into short shapes and then built a narrative around an API call that happened to be in the trace but was not the causal link. The result was a plausible-sounding but incorrect diagnosis that I nearly acted on.
How it surfaced during a real debugging session
The failure appeared during an intermittent outage: a component would crash under load and then come back. I pasted the truncated trace into a multi-turn session to iterate towards a repro. The model quickly suggested changing how we acquired a shared resource and provided a small patch. I continued the conversation in chat style, asking follow-ups, and the assistant reinforced the same path — it had locked on to one frame as “the culprit.”
Because the suggested change was small and non-invasive, a developer implemented it in a hotfix branch. Unit tests passed, and CI was green. The intermittent failures continued. Only after instrumenting the service and capturing the full, untruncated trace did we see a suppressed exception earlier in the chain indicating a race condition. The assistant had never cited that suppressed cause because the summary process had effectively thrown it away.
Why this behavior is subtle and easy to miss
Stack traces are structured but noisy: they include system frames, library frames, and often chained causes. LLMs trained on many examples learn shortcuts that work most of the time; they prioritize salient text snippets and common failure patterns. When you paste a truncated or prettified trace they can omit the chaining behavior or misattribute the root cause to the most semantically obvious frame. That makes the output both readable and dangerously oversimplified.
Two small model behaviors compound: confident-sounding summaries and an inclination to generalize from patterns. Those lead to narratives that hide uncertainty. As humans, we tend to trust readable explanations. The combination means an engineer can accept a model’s brief fix without creating an adequate reproducer or adding instrumentation, causing the erroneous diagnosis to propagate through commits and reviews.
Practical mitigations and adjusted workflows
I now treat any stack-trace-based suggestion as a draft hypothesis, not an answer. A few low-cost habits help: always paste the full trace (don’t truncate chained exceptions), ask the model to list its assumptions explicitly, and require an instrumentation plan before applying changes. I also rely on tools for verification and citation; when I need authoritative cross-checks I use a focused verification workflow such as deep research to collect references and reproduce steps rather than accepting a single-turn fix.
These practices don’t eliminate the failure mode, but they turn the LLM from a source of instructions into a collaborator that proposes hypotheses to test. In practice that means more logging, short-lived feature flags for hotfixes, and a checklist that requires reproducer or metric improvement before rolling changes to production. Treating AI output as a probabilistic draft keeps debugging rigorous and prevents subtle misreadings of traces from becoming expensive regressions.
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