What does a real RAG pipeline look like in production?
In this talk at @7CTOs, I walk through:
→ Chunking strategies
→ Search (BM25 + vectors)
→ Query rewriting, HyDE
→ Reranking, repacking
→ Tool calls
It’s a full-stack view of modern LLM app design.
- Hot take: AI startups relying solely on OpenAI, Anthropic, or Gemini are as vulnerable as the old social startups that were entirely dependent on Facebook's API. One API change could spell disaster. Everyone in this space should experiment with running their own models.
- 1/ Observations of a vibe coder 🧵 I’ve been vibe coding for a while now — side projects, little work utilities, messing around. Here are some things I’ve learned. 👇
- Not all chunks are created equal. In my latest blog post, I break down how Benchmate approaches chunking for RAG—from simple token splits to full semantic clustering. “An ideal chunk is one answer to one question.” Read more: app.scientist.com/blog/2025/04/3… #RAG #LLM
- New post is live: “Data Sources and Retrieval-Augmented Generation (RAG)”! Dive into how high-quality data sources transform AI-driven insights in pharma research. #AI #RAG #Pharma #DrugDiscovery app.scientist.com/blog/2025/04/0…

