The weighted sum approach to hybrid search is fragile and breaks in production. This article introduces the Nomination → Union → Selection architecture: a deterministic pipeline that combines dense and sparse search without magic weights, then uses a Cross-Encoder to rerank results with surgical precision. Learn how to build RAG systems that scale.
Dense vectors are magical at capturing semantics, but they fail when you need exact matches. This article unpacks the Vocabulary Mismatch Problem and introduces SPLADE—a neural approach that combines the precision of keyword search with the intelligence of transformers. Learn why sparse embeddings matter and how to architect hybrid search for production.
Building production RAG systems is fundamentally an ETL (Extract, Transform, Load) challenge. We explore why documents must be treated as hierarchical data structures, not string soup. Discover structure-aware splitting, metadata injection, and multi-resolution indexing strategies that transform data quality and eliminate hallucinations.
We are watching the AI industry commit the original sin of the web all over again. For the last two years, we’ve obsessed over Context Engineering, treating Agents like static, PHP-era websites. When a user asks a question, the system performs a “database fetch” on demand, pulling context just in time to generate an answer. We haven’t reinvented software; we’ve just replaced the mouse click with a prompt, keeping the same brittle, pull-based architecture underneath....
Conventional keyword search matches user query words to document words using an inverted index data structure for efficient matching and ranking by relevancy.
Retrieval is the backbone of RAG. We explore the critical steps often missed by developers: proper chunking strategies, the ‘Librarian’ analogy for vector vs. keyword search, and solving the math problem of Hybrid Search using Reciprocal Rank Fusion (RRF).
LLMs revolutionize question-and-answer systems with exceptional language understanding and creative writing skills. Lossy compression during training may make retrieving information challenging. Leveraging LLMs’ language expertise transforms building question-and-answer systems into reading comprehension using their ability to comprehend text, forming the basis for RAG systems that shift question answering to efficient knowledge base searches.