LLM-as-a-Judge

Part 5: Multi-Dimensional Agent Evaluation & LLM-as-a-Judge Harnesses Answer-First: Production multi-agent evaluation requires multi-dimensional grading rubrics, LLM-as-a-Judge harnesses, and trace trajectory analysis. Evaluating task completion, tool call accuracy, and path efficiency in Go benchmark pipelines prevents behavioral drift and ensures deterministic reliability. Evaluating autonomous agent systems presents unique architectural challenges distinct from traditional software testing and static model benchmarking. While standard software unit tests verify deterministic inputs against exact output matches, multi-step LLM agents exhibit non-deterministic reasoning paths, dynamic tool selection, and complex conversation states. Relying solely on final answer accuracy masks hidden regressions such as redundant tool invocations, inefficient context expansion, or subtle prompt injection vulnerabilities. ...

Part 10 — Production Evals & CI/CD Guardrails: LLM-as-a-Judge at Scale In traditional software development, continuous integration (CI) relies on deterministic unit and integration tests. A function either returns the expected string or it fails the build. In GenAI and RAG engineering, responses are non-deterministic. A minor adjustment to a system prompt, a change in vector embedding models, or an update to chunking strategy can silently degrade response quality, introducing subtle hallucinations or dropping key context facts. ...