AI & Machine Learning

Build Once, Sell Twice: caching LLM analysis with pgvector

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Using pgvector for caching large language model (LLM) analysis significantly reduces costs by avoiding redundant LLM processing of the same data from different angles. This approach ensures that expensive analyses are performed only once, with subsequent queries leveraging precomputed embeddings stored in PostgreSQL, improving margins and scalability. Developers should consider implementing similar caching strategies to optimize AI-driven feature costs.

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Build Once, Sell Twice: caching LLM analysis with pgvector

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