Cutting-Edge Search Techniques
Beyang and Patrick discuss the limitations of sophisticated AI-assisted search techniques and the importance of context retrieval in optimizing search results. They explore the challenges of mapping concepts in embedding space and the potential for cross-cutting concerns in developing effective search stacks.In this clip
From this podcast
Unsupervised Learning
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
Related Questions
What are the best approaches for AI coding assistants to get context in a large codebase, as discussed in the episode 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 and the clip Contextual Model Challenges?
What do you think about adding context to the embeddings in the episode RAGKit with Kyle Davis - Weaviate Podcast #93! and the clip Code Embeddings Exploration? How does adding context help with the embedding process?
What are the best approaches for AI coding assistants to get context in a large codebase, as discussed in the episode 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 and the clip Contextual Model Challenges?