Rag

A vector database stores vectors alongside identifiers and metadata, indexes them for similarity search, and provides database capabilities such as persistence, filtering, updates, access patterns, and operational management. Its purpose is broader than running a nearest-neighbour algorithm. That distinction separates a database from a library such as FAISS. FAISS provides efficient vector indexing and search primitives. It does not, by itself, provide the complete persistence, multi-tenancy, metadata, authorisation, backup, replication, and service-management layer expected from a production database. ...

A knowledge graph represents entities and the relationships between them in a graph-shaped data model. Its value is not that graphs automatically contain truth or make systems explainable. The value is that identity, relationships, provenance, and domain meaning can be made explicit and queried together. That distinction matters in applied AI. A graph built from noisy documents can preserve extraction errors. A graph query can return an irrelevant path. An ontology can support valid inference from false assertions. A language model can still misread correctly retrieved facts. Knowledge graphs create useful structure; reliability comes from the evidence, controls, and evaluation around that structure. ...

A public benchmark can help compare foundation models. It cannot tell you whether a document assistant retrieves the right policy, whether an agent calls a tool with safe arguments, or whether a release stays within its latency budget. For an applied AI system, evaluation is a decision process: Given a defined workload and risk tolerance, is this version safe and useful enough to release? That question changes the unit of evaluation. The object under test is not only the model. It is the complete system: prompts, retrieval, tools, orchestration, guardrails, model configuration, and user interface. ...

Retrieval-augmented generation (RAG) gives a language model access to external evidence at inference time. It is useful when answers depend on private, changing, or domain-specific information that should not be encoded only in model weights. RAG does not make an answer correct by construction. A system can retrieve the wrong document, omit a decisive table row, use an obsolete version, misunderstand accurate evidence, or attach a citation that does not support its claim. Retrieval changes the failure surface; it does not remove it. ...

“Memory” is an overloaded word in large language model systems. It can refer to information encoded in model parameters, tokens supplied in the current request, cached attention tensors, retrieved documents, recurrent state, or records stored by an application. These mechanisms have different lifetimes, costs, failure modes, and privacy implications. Treating them as one feature leads to architectural mistakes. A larger context window does not create cross-session memory. A key–value (KV) cache does not make a model remember a user. Retrieval does not update model parameters. A product that recalls a preference after a week is using storage and orchestration outside the base model unless the model itself has been retrained or edited. ...