Debugging LLM Pipelines in Python- Engineering Lessons from the Trenches

# Abstract: LLMs are powerful but things can quickly go wrong when you’re building real apps. Prompts may fail, tool calls can break, and outputs often behave in unexpected ways. In this talk, we’ll look at real-world issues developers face when working with Python-based LLM pipelines and how to fix them. You’ll walk away with practical debugging tips and tools to help make your AI apps more stable and trustworthy.

# Description: This talk shares lessons learned from hands-on experience building LLM-powered applications using Python. We'll walk through the common problems that show up in the real world like broken toolchains, failing prompts, inconsistent responses, and unexpected agent behaviors.

The session focuses on practical debugging strategies: how to trace issues, log properly, test prompts like code, and use tools like langsmith or simple logs to spot problems early. We’ll also touch on memory management, retry logic, and why some issues only show up after your app goes live. Whether you're using LangChain, CrewAI, or your own pipeline logic, this talk will give you the mindset and techniques to debug faster, work smarter, and build more reliable AI systems.

No heavy theory just real engineering practices from the field.

• Know where and why LLM apps break in real workflows

• Use open-source tools to debug prompts, agents, and memory

• Apply versioning, logging, and testing strategies to prompt chains

• Learn debugging practices that improve developer productivity