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aiguideThe RAG Techniques Compendium

Semantic Caching: Run the RAG Pipeline Only Once for Semantically Similar Queries

Caching doesn't have to match exact query strings -- semantically similar questions can hit the cache too, skipping the entire RAG pipeline execution.

#rag#semantic-cache#caching#vector-search#performance
aiguideThe RAG Techniques Compendium

SPLADE: Smarter Sparse Vector Search Beyond BM25

BM25 only recognizes words that appear in the query. SPLADE infers related terms and adds them to the search, gaining partial semantic capability while preserving the precision of keyword search.

#rag#splade#sparse-vector#bm25#retrieval#hybrid-search
aiguideThe RAG Techniques Compendium

Text-to-SQL Router: Precise Queries That Skip RAG

Questions like 'how many routes did I complete this year' will never be answered well by RAG semantic search — querying the database directly is far more accurate. Let the LLM identify intent, extract parameters, and execute predefined SQL templates.

#rag#text-to-sql#sql#query-routing#structured-query
aiguideThe RAG Techniques Compendium

Vector Database Selection: How to Choose Between Pinecone, Weaviate, Qdrant, and Vectorize

Vector database selection is more constrained by deployment platform than LLM selection. Determine your platform and scale requirements first, then evaluate features — don't just look at benchmarks.

#rag#vector-database#pinecone#weaviate#qdrant#cloudflare-vectorize