Building LLM Powered Applications in Production - Hamza Farooq | Vector Space Talks - Qdrant
Jan 9, 2024Demetrios Brinkmann

Summary
This post discusses building LLM-powered applications in production, focusing on complex search, conversational search, and personalization. It highlights challenges in LLM service downtime and strategies for mitigation, as well as the unique approach of Traversal AI in creating unified platforms for LLM integration and domain-specific search. The importance of user experience (UX) in GenAI product development is also emphasized.
More from the Filtered Vector Search trail
- How Lucy Built a Source-of-Truth Retrieval Layer for 2.8M+ SEC and DART Filings with Qdrant - Qdrant
- How Himalayas Built Semantic Job Matching for 100,000+ Remote Listings with Qdrant - Qdrant
- How to Tune Vector Search Without Guessing - Qdrant
- Pre-Filtering vs Post-Filtering (and Why Qdrant Does Neither) - Qdrant
- Filtered Vector Search: What ACORN Fixes, and What Fixes ACORN - Qdrant