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5 Things AI Engineers Must Evaluate: RAG, Agents, Models, Data, Guardrails

Bashiri Smith · Facebook reel · 2026-09-30 · 0:33 · 11,414 views · Open on Facebook

Topics: Evaluation (Evals) & Testing, Retrieval-Augmented Generation (RAG), AI Safety, Security & Guardrails · Level: beginner

Summary

A quick checklist of the five parts of an AI system that engineers should evaluate, and the two metrics to check for each: RAG, agents, models, datasets and guardrails. The creator says evaluation is one of the most important skills for building production AI systems. He points viewers to his free AI Evaluation Guide and his BASWE AI Engineer community.

Key points

Resources mentioned

From the PDF shared here: Evaluation Field Guide (baswe.ai engineer accelerator – Ops and Evaluation module)

Open the original · 41 pages

Bashiri Smith's 41-page practitioner's guide to evaluating AI systems. It covers eval fundamentals (eval-driven development, golden datasets, metric types, basic statistics), deterministic and overlap metrics for LLM outputs, LLM-as-a-judge (rubric design, judge biases, checking judges against human labels), RAG evaluation (retrieval metrics and the RAG triad), agent trajectory evaluation, model benchmarking and fine-tune evaluation, and production evals (tracing, online signals, A/B tests, drift, guardrails, CI eval gates). It ends with a map of the eval tool stack, four portfolio projects and interview signals. Read it once end to end, then use it as a reference, and build one of the portfolio projects.

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