Five hard earned lessons about Evals — Ankur Goyal, Braintrust

AI Development Strategy - Building successful AI applications requires a sophisticated engineering approach beyond just writing good prompts [1] - The industry emphasizes the importance of evaluations (evals) as a core component of the development process [1] - Evaluations should be intentionally engineered to reflect real-world user feedback and drive product improvements [1] Technical Focus - "Context engineering" is emerging as a new frontier, focusing on optimizing the entire context provided to the model [1] - Context engineering includes tool definitions and their outputs [1] - The industry advocates for a flexible, model-agnostic architecture [1] Adaptability - The architecture should quickly adapt to the rapidly evolving landscape of AI models [1] - Optimize the entire evaluation system, not just the prompts [1]