How do you build an effective eval system for AI pipelines? This episode delves into the common misconception of evals as a magic solution and outlines practical steps to develop them incrementally. From managing the complexity of AI systems without golden datasets to understanding the real-world role of evals in stabilizing your production pipeline, this session offers a deep dive into a crucial aspect of AI engineering.

Key highlights include:

• Transitioning from vibe evaling to structured evaluation systems tailored to your domain's needs.
• Implementing runtime checks for structured data extraction and validating against known truths in financial and document data pipelines.
• Designing robust evals for string generation tasks like lesson plans using categorical evaluations rather than arbitrary confidence scores.

The session mainly works within the framework of Python and BAML, discussing considerations such as structured outputs versus raw model calls. It stands alone but complements earlier discussions on AI infrastructure design.