Overview
Syllabus
- Introduction
- Denis describes Perplexity as an answer engine.
- Discussion on using third-party APIs and in-house infrastructure.
- Choosing between In-house vs. outsourced models
- Evaluating the quality of results and using LLMs.
- Building a classical search engine and custom parsers.
- Latency and quality trade-offs in providing answers.
- Handling controversial domains and providing unbiased answers.
- Importance of hiring diverse annotators and their influence.
- Denis's story about pitching Yann LeCun.
- Early signs of success for Perplexity's gen AI application.
- The hardest parts of scaling up the application and maintaining focus on speed and accuracy
- Closing thoughts
Taught by
Weights & Biases