Developer finds deterministic rules outperform LLM in log classification after rigorous eval
A software developer built an evaluation harness to test whether a large language model could reliably classify integration failures from a legacy system generating over 4,000 parsed log errors. Testing across 58 hand-reviewed cases showed rule-based classification scored 89.7% accuracy versus the LLM's 87.9%, and three rounds of prompt engineering produced no improvement. The developer also found that regex matched the LLM's structured data extraction at 90–97% agreement, disproving an earlier assumption that extraction was a natural fit for AI. The final architecture uses deterministic rules for classification and structured extraction, reserving the LLM solely for writing plain-English incident summaries, where it scored 4.62 out of 5 for faithfulness. The entire pipeline runs locally via Ollama at zero API cost, processing each case in roughly 8–10 seconds.
This is an AI-generated summary. ShortSingh links to the original source for the complete article.
Discussion (0)
Log in to join the discussion and vote.
Log in