Even with careful pre-filtering and scope reduction, LLM outputs in production can still contain errors, such as claiming no data exists when data is actually available. This article presents a post-hoc correction approach with three components: tool reinforcement to ensure proper tool usage, response validation to catch inconsistencies, and dual-timestamp fallback to handle timing issues. The combined techniques yield 5-9 percentage point improvements in key metrics. More importantly, they transform scattered online error reports into automated recovery workflows. For teams running LLM-based applications in production, these patterns offer a practical safety net that complements upfront prompt engineering and guardrails. The approach is particularly valuable for business-critical applications where incorrect outputs can lead to poor decisions or user trust erosion.
Systematic post-hoc correction strategies for LLM errors in production, yielding 5-9% improvement.