The 8 Layers of Evaluating Production RAG and Agent Systems

Bashiri Smith · Facebook reel · 2026-08-26 · 0:07 · 7,817 views · Open on Facebook

Topics: Evaluation (Evals) & Testing, Retrieval-Augmented Generation (RAG), AI Agents, Tool Use & MCP · Level: intermediate

Summary

Building a RAG app or agent demo is only half of an AI engineer's job. The other half is showing the system actually works, using a repeatable evaluation process that catches improvements, regressions and quiet failures before users do. The video lists the questions a strong AI engineer should be able to answer and eight layers of evaluation, from retrieval evals to online production monitoring.

Key points

• To get hired, you need to show you can prove a system works, not just build a demo.
• Questions to answer: Is the retriever finding the right info? Is the answer grounded in the retrieved context? Does the agent pick the right tools and arguments? Does the workflow finish the task?
• Test how the system behaves with missing data, conflicting instructions and unexpected inputs.
• Catch regressions after any change to the prompt, model, chunking strategy or agent workflow.
• Weigh accuracy against latency and cost when you evaluate.
• Eight evaluation layers: (1) retrieval evals, (2) response quality and faithfulness, (3) tool-calling and agent trajectory evals, (4) end-to-end task success, (5) deterministic checks and unit tests, (6) LLM-as-a-judge, (7) human evaluation, (8) online monitoring and production feedback.
• Aim for a repeatable evaluation system that tells you whether the app is improving, getting worse or failing in ways users would eventually notice.

Resources mentioned

• Evaluation Field Guide (baswe.ai engineer accelerator – Ops and Evaluation module) · pdf · drive.google.com · free
  Bashiri Smith's 41-page practitioner's guide to evaluating AI systems.
  Also in: 5 Things AI Engineers Must Evaluate: RAG, Agents, Models, Data, Guardrails (Bashiri Smith on Facebook · notes), Evaluation for AI Engineering: Free Full Guide (Resource Share) (Bashiri Smith on Facebook · notes), Why Evaluation Is the Skill That Sets Top AI Engineers Apart (Bashiri Smith on Facebook · notes)

Try this

• Comment "EVALS" on the reel to get the creator's full evaluation guide.
• For your RAG or agent project, set up evals for retrieval, faithfulness, tool calls and agent trajectories, and end-to-end task success.
• Add deterministic checks and unit tests, LLM-as-a-judge scoring, and human evaluation.
• Set up regression testing so you can measure the effect of changes to prompts, models, chunking or workflows.
• Track accuracy, latency and cost together, and add online monitoring with production feedback.
• Take a basic RAG app or agent and add a repeatable evaluation suite that covers all eight layers, so you can show it works and catch regressions.