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Guardrails and evaluation
Without an evaluation set you cannot tell an improvement from a regression. Guardrails then enforce the limits your evaluation exposed.
Lesson 3.37m 18sIntermediate15.5k students
Chapters
7:18- 0:00Why AI needs guardrails in production
- 0:35What we’re building: self-correcting local AI
- 1:20The stack: LangChain + LCEL + Streamlit + Llama 3 (Ollama
- 2:10Designing the 3-stage flow (Generate → Evaluate → Repair
- 3:25Writing evaluation criteria (what “good” looks like
- 4:40Building the evaluator chain in LCEL
- 5:45Designing the repair prompt (fix + upgrade output
- 6:55Wiring evaluator → repair into a reusable guardrails workflow
- 8:05Streamlit UI: show each step + debug the pipeline
- 9:10Testing failure cases (incomplete, wrong, messy formatting
- 10:20Practical use cases (agents, automations, production tools
- 11:10What’s next: deeper evaluation + long-form QC (EP12
- 11:35Subscribe + comment what to evaluate first
What this lesson covers
- Build a small, honest evaluation set early
- Score changes against it before shipping
- Enforce boundaries the model must not cross
This lesson sits in Tool Calling and Agents, part of Building AI Apps with LLMs. It assumes what came before it and leads directly into the next lesson in the module.
In this lesson you will
- Build a small, honest evaluation set early
- Score changes against it before shipping
- Enforce boundaries the model must not cross
Resources
Personal notes are coming soon.