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

evaluating RAG is the part nobody talks about, the triad is super handy

THE PROBLEM Most training — human or AI — tells people the outcome and then pretends they learned the decision. It also breaks the moment the interface gets complicated, which is why a lot of training tools look impressive and teach very little.

WHAT I BUILT An adaptive, rule-based training system for day trading, developed iteratively from real failure in the interface itself:

Hidden outcomes — the learner commits to a call before seeing what happened

Structure-first grading, with indicators (EMA / Fibonacci / VWAP) as secondary confluence, not the decision itself

Deliberately hard cases — near-invalidation and controlled false alarms, so learners can't just pattern-match easy examples

Failure-driven UI iteration: when interaction reliability failed, a chat-native fallback preserved the training function, and a later stable HTML implementation became the preferred version — function over flash

WHAT I CAN DO FOR A CLIENT

Design an AI-powered trainer / simulator for a skill your team or customers need to learn (sales calls, compliance decisions, technical diagnosis, trading, operations, onboarding)

Build grading logic that evaluates reasoning and process, not just right/wrong answers

Design practice case libraries — including the hard, ambiguous, edge cases that actually build competence

Rescue or redesign a training tool that looks good but isn't teaching