vector-database
5 posts — newest first.
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Your runbooks are lying to your agents: RAG for on-call knowledge
On-call agents are only as good as what they retrieve. Making runbook RAG fresh, exact-match aware, and poison-resistant — with a runnable starter repo.
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The Five Types of Agentic Memory (and When to Use Each)
Agentic memory is five things — working, episodic, semantic, procedural, entity — each with its own storage, eviction, and failure mode. A decision guide.
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What are vector embeddings?
A primer on vector embeddings — how meaning becomes something you can search, cluster, and compare, and the failure modes you only see in evaluation.
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The CAP theorem in AI-native distributed systems
CAP didn't get repealed when LLMs showed up. How the C/A/P trade-offs shift when the datastore is a vector index, context graph, or retrieval layer.
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What is Retrieval-Augmented Generation (RAG)?
A primer on Retrieval-Augmented Generation — grounding an LLM's answer in documents you trust. Indexing, serving, and the failure modes that bite.