Learn practical, data-driven methods to quickly evaluate and improve AI applications.
- How do I test applications when the outputs are stochastic and require subjective judgements?
- If I change the prompt, how do I know I'm not breaking something else?
- Where should I focus my engineering efforts? Do I need to test everything?
- What if I have no data or customers, where do I start?
- What metrics should I track? What tools should I use? Which models are best?
- Can I automate testing and evaluation? If so, how do I trust it?
If you aren't sure about the answers to these questions, this course is for you.
- Fundamentals & Lifecycle of LLM Evaluation
- Systematic Error Analysis
- Implementing Effective Evaluations
- Collaborative Evaluation Practices
- Architecture-Specific Strategies
- Production Monitoring & Continuous Evaluation
- Efficient Continuous Human Review Systems
- Cost Optimization Techniques
- Engineers & technical PMs building AI products.
- Developers seeking rigorous evaluation beyond basic prompt tuning.
- Teams aiming to automate and trust their AI testing.