Product, business & communication
Define quality metrics and eval plans for AI features
Choose offline/online metrics, design human-rating rubrics, set launch bars for AI features.
~10 focused hours·intermediate
Tools: LangSmith/LangFuse, OpenAI/Anthropic evals, Label Studio (human rating), spreadsheets/notebooks for rubric scoring
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Choose offline metrics (accuracy, groundedness, exact/fuzzy match) appropriate to the task
- Design a human-rating rubric that two raters apply consistently
- Set an online metric and a launch bar tied to business outcome, not just model score
- Build a small labeled eval set from real or representative data before launch
- Track eval scores across prompt/model versions to justify a change
- Explain the gap between an offline eval score and real user satisfaction
- Design a human-in-the-loop review process for low-confidence outputs
Needs first: Explain how LLMs work and where they fail
Learn — free, link-checked
The few resources that matter
Read · intermediate · 40 min · cookbook.openai.com
Getting Started with OpenAI Evals
Runnable notebook building a graded eval from scratch, copy this pattern straight into a CI regression suite. — OpenAI
Read · intermediate · 40 min · docs.smith.langchain.com
Evaluation
Shows how to build labeled datasets and run automated regression evals so RAG quality doesn't silently degrade. — LangChain
Course · intermediate · 90 min · deeplearning.ai
Evaluating and Debugging Generative AI
Shows how to version prompts/configs and trace experiments so eval regressions are debuggable, not just scored. — DeepLearning.AI (with Weights & Biases)
Build from · intermediate · 90 min · github.com
OpenAI Cookbook
Battle-tested example notebooks (chat apps, vision, retries) you can lift directly into a real project. — OpenAI
Practice
Eval harness for a Hindi-support-ticket auto-classifier
Build a small eval set (50-100 examples) of Hindi/Hinglish customer support tickets labeled by category (billing, technical, refund, etc.), then build an LLM-based classifier and score it against the set using both an automated metric (accuracy/F1) and a human-rating rubric for edge cases. Track results across 2-3 prompt variants and write up which one you'd ship and why, including the launch bar you'd require.
Done when
- Eval set has at least 50 labeled examples with a documented labeling guideline
- Results are reported for at least 2 prompt/model variants with a comparison table
- A human-rating rubric (not just automated accuracy) is defined and applied to a sample of outputs
- A written recommendation states the launch bar and whether the current best variant meets it
Prove it
Evidence a recruiter can check
- Public repo with the eval set, harness code, and a results table
- A short write-up explaining the rubric and why the chosen metric fits the task
- Before/after comparison showing an eval-driven prompt improvement
Interview
Questions you'll get asked
- How would you evaluate a customer-support chatbot before launch — walk me through your plan.
- What's the difference between an offline eval and an online (in-production) metric, and when do you need both?
- How do you build a rubric that two different human raters will score consistently?
- Tell me about a launch bar you set for an AI feature — how did you pick the threshold?
- How would you detect that a model update silently made quality worse?
- What metrics would you track for a RAG system beyond 'did it answer'?