Ep 33: CTO and Co-Founder of Sourcegraph on Current Landscape and Future of Software Development, How to Make RAG Better, and Building Towards the Agentic Future

Search Evolution

The evolution of search and retrieval systems is a critical aspect of AI-driven technologies. Patrick Chase and Jacob Effron discuss how techniques like vector search have improved significantly, making them more useful and effective for companies like Sourcegraph 1. Beyang Liu emphasizes the importance of starting with simple keyword searches, which are easy to implement and iterate upon, before moving to more complex strategies 2. He notes that understanding user intent is crucial for refining search strategies, drawing parallels to Google's approach of using user behavior to enhance search quality 3.

You need to think a lot about the rag pipeline. The retrieval mechanisms matter a lot.

--- Beyang Liu

This focus on user-centric design ensures that search systems remain relevant and effective.

   

Model Evaluation

Evaluating AI models is essential for improving coding applications and enhancing user experience. Beyang Liu highlights the significance of latency and local inference in developer experience, noting that even milliseconds can impact productivity 4. He advocates for establishing simple baselines before pursuing complex models, as this approach often yields better results and facilitates rapid iteration 5. Liu shares a philosophy of starting with straightforward solutions to understand the problem space better before delving into more sophisticated methods.

You always go with the baseline. You always do a mixture of Gaussians or even simpler baseline.

--- Beyang Liu

This pragmatic approach allows for flexibility and adaptability in model development.