Completed "Building and Evaluating Advanced RAG" by DeepLearning.AI (TruEra, LlamaIndex).

The most useful part wasn't building another RAG pipeline, it was learning how to actually evaluate one. The RAG Triad (answer relevance, context relevance, groundedness) gives you a way to catch exactly where a system is failing: bad retrieval vs. the model making things up vs. just answering the wrong question.

Also went through sentence-window and auto-merging retrieval, two ways to fix the classic RAG trade-off between precise matching and having enough context to actually answer well.

Applying this directly to a RAG tool I'm building for equity research. Evaluation is the part of RAG most people skip, and it's exactly where things quietly break in production. https://www.deeplearning.ai/accomplishments/491d7a76-f89d-4e13-9d6b-3377fced78a2?accomplishmentId=491d7a76-f89d-4e13-9d6b-3377fced78a2&usp=sharing