Contextual Retrieval for RAG, Step by Step
Vector search misses chunks that lost their document context. Contextual Retrieval prepends a short LLM-written summary to each chunk before you embed it.
#Embeddings(5)
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Product Quantization for Vector Search
HNSW makes vector search fast, but the embeddings still fill your RAM. Product quantization compresses them ~32x with a small recall hit. Here is how it works.
Matryoshka Embeddings: Smaller Vectors for RAG
Matryoshka embeddings front-load meaning into a vector's first dimensions, so you can truncate them for cheaper, faster RAG search without losing much recall.
How to Choose an Embedding Model for RAG
The embedding model sets the ceiling on RAG retrieval quality. How to choose one by task fit, sequence length, dimensions, and domain, plus the silent bugs.
How to Build a Semantic Cache for LLM Apps
Exact-match caching misses paraphrases, so LLM bills stay high. Here is how to build a semantic cache with embeddings, a similarity threshold, and its traps.