Research Report 4.1: Vector Databases and Embeddings
How text becomes vectors, how similarity search actually works, and why understanding the mathematics behind embeddings is the difference between a RAG system that retrieves relevant documents and one that retrieves plausible-sounding garbage
Research report covering the technical foundations of vector databases and embeddings in LLM orchestration - embedding generation mechanics, vector mathematics (cosine similarity, euclidean distance, dot products), indexing strategies (HNSW, IVF, LSH) and their performance tradeoffs, approximate nearest neighbor algorithms, hybrid search combining vectors with BM25, major vector database comparison (Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector), and the failure modes that distinguish production RAG from tutorial RAG.
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How text becomes vectors, how similarity search actually works, and why understanding the mathematics behind embeddings is the difference between a RAG system that retrieves relevant documents and one that retrieves plausible-sounding garbage
Documentation on claude 22 research report 7.2! performance & optimization
The project documentation for a 23-report research initiative that explains how LLM systems actually work - from transformer mechanics through multi-agent coordination, built for technical leaders who need accurate mental models rather than vendor marketing
Every AI product you use runs on the same core mechanism - a pattern-matching engine that processes entire sentences simultaneously instead of word by word, and understanding how it works changes how you build with it
How text becomes vectors, how similarity search works, and why vector databases are the backbone of semantic retrieval.