Production-Ai

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. ...

An LLM becomes an agent when its outputs can change an environment: querying a database, editing a record, sending a message, running code, or asking a person for approval. The model remains important, but the production system around it determines whether those actions are valid, authorised, repeatable, and observable. This distinction matters. A capable model inside a weak control loop can execute the wrong tool, repeat an irreversible action after a timeout, or follow malicious instructions hidden in retrieved content. A dependable agent therefore needs more than prompting. It needs an explicit action interface, an execution policy, state management, safety boundaries, and evaluation over complete trajectories. ...