Ask HN: How Do You Debug Multi-Step AI Workflows When the Output Is Wrong?

Debugging multi-step AI workflows is one of the most challenging aspects of deploying local LLMs, especially when chaining multiple inference calls or building complex agentic systems. This Hacker News discussion captures community approaches to identifying where workflows fail—whether in prompt engineering, model selection, or pipeline architecture.

For local LLM practitioners, the absence of built-in observability in self-hosted systems makes debugging particularly critical. Unlike cloud-based APIs with logging infrastructure, on-device inference requires developers to instrument their own monitoring, logging, and rollback mechanisms to understand failure modes.

The discussion at Hacker News likely covers practical techniques like output validation, intermediate step inspection, A/B testing different models, and systematic isolation of problematic components—all essential skills for production local inference systems.

Source: Hacker News · Relevance: 7/10