How to Benchmark a Vector Database
Benchmarking a vector database means measuring how well it returns the right nearest neighbors under realistic load, not…
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Structured articles covering everything from core concepts to production operations.
Sizing and Capacity Planning for AI Databases
Sizing an AI database starts with a simple estimate: vector count multiplied by vector dimension multiplied by bytes…
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Self-Hosted vs Managed Vector Databases
Self-hosted vector databases give teams the most control over infrastructure, data placement, configuration, and long-term cost, but they…
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Running Vector Databases in Production
Running a vector database in production is different from running one in a prototype because the system has…
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Common RAG Failure Modes and How to Fix Them
RAG systems usually fail for practical reasons: the right information is split badly, retrieved weakly, packed into the…
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Semantic Caching for LLM Applications
Semantic caching helps LLM applications reuse answers when a new query means the same thing as a previous…
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GraphRAG: Combining Knowledge Graphs and Vectors
GraphRAG combines vector retrieval with knowledge graph relationships so an AI system can retrieve not only semantically similar…
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Multimodal RAG: Retrieving Text and Images for Better AI Answers
Multimodal RAG is retrieval-augmented generation that can search across more than one type of content, most commonly text…
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Evaluating RAG Quality
Evaluating RAG quality means measuring whether a retrieval-augmented generation system finds the right information, ranks it well, and…
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Reducing Hallucinations with Grounding
Grounding reduces hallucinations by giving an AI system retrieved evidence to use before it answers. Instead of relying…
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