The hallucination issue is real, but we've had success with a hybrid approach:
1. LLMs are excellent at pattern recognition and initial triage - finding anomalies, categorizing error types, correlating timestamps across services
2. For root cause analysis, we use them as "research assistants" rather than authoritative sources. They suggest investigation paths: "check if disk I/O spiked before the timeout errors" or "this pattern often indicates connection pool exhaustion"
3. Structured prompting helps: instead of "analyze this error," we ask "what are the 3 most likely causes of this specific error pattern, and what logs would confirm each hypothesis?"
4. The real win isn't replacing human analysis, it's making engineers 10x faster at sifting through massive log volumes to find the signal in the noise.
Works especially well when you can provide system context in the prompt - service topology, recent deployments, known issues. The LLM becomes much more accurate when it understands your specific environment.
sollewitt 9 minutes ago [-]
But does it work? I’ve used LLMs for log analysis and they have been prone to hallucinate reasons: depending on the logs the distance between cause and effects can be larger than context, usually we’re dealing with multiple failures at once for things to go badly wrong, and plenty of benign issues throw scary sounding errors.
dbreunig 4 minutes ago [-]
Check out “Recursive Language Models”, or RLMs.
I believe this method works well because it turns a long context problem (hard for LLMs) into a coding and reasoning problem (much better!). You’re leveraging the last 18 months of coding RL by changing you scaffold.
verdverm 22 minutes ago [-]
This is one of those HN posts you share internally in the hopes you can work this into your sprint
Rendered at 16:14:41 GMT+0000 (Coordinated Universal Time) with Vercel.
1. LLMs are excellent at pattern recognition and initial triage - finding anomalies, categorizing error types, correlating timestamps across services
2. For root cause analysis, we use them as "research assistants" rather than authoritative sources. They suggest investigation paths: "check if disk I/O spiked before the timeout errors" or "this pattern often indicates connection pool exhaustion"
3. Structured prompting helps: instead of "analyze this error," we ask "what are the 3 most likely causes of this specific error pattern, and what logs would confirm each hypothesis?"
4. The real win isn't replacing human analysis, it's making engineers 10x faster at sifting through massive log volumes to find the signal in the noise.
Works especially well when you can provide system context in the prompt - service topology, recent deployments, known issues. The LLM becomes much more accurate when it understands your specific environment.
I believe this method works well because it turns a long context problem (hard for LLMs) into a coding and reasoning problem (much better!). You’re leveraging the last 18 months of coding RL by changing you scaffold.