LLM EngineeringChoosing the LLM judge for evaluation pipelinesHow to pick the LLM that grades your LLM. The cost-quality tradeoffs, the calibration check, and why a weaker judge is sometimes the right call.EvaluationLLMMetrics+2 moreRead Article8 min
LLM EngineeringGround truth vs relevancy in RAG evaluationWhy ground truth and relevancy measure different things in RAG evals. When to use each, how to build both datasets, and the 2 metrics that matter most.RAGEvaluationMetrics+2 moreRead Article9 min
LLM EngineeringHallucination testing for RAG pipelinesHow to test a RAG pipeline for hallucinations systematically. Adversarial prompts, the out-of-scope set, and the metric that catches confabulation.RAGEvaluationLLM+2 moreRead Article8 min
LLM EngineeringTesting and evaluating RAG pipelines end to endHow to test a RAG pipeline like real software. Unit, integration, and eval tests that catch regressions before they ship. The 3-layer test strategy.RAGEvaluationProduction AI+2 moreRead Article8 min
LLM EngineeringFact-checking RAG answers: grounding with verificationHow to fact-check RAG answers with a second LLM pass that verifies every claim against the retrieved context. The prompt, the rejection rule, and the loop.RAGLLMEvaluation+2 moreRead Article8 min
LLM EngineeringRetriever k-value tuning for RAG: the right top-kHow to pick the right k value for your RAG retriever. The 3-step tuning process, the failure modes of k=3 and k=20, and the sweet spot in between.RAGVector DatabasesEvaluation+2 moreRead Article8 min
AI Engineering in PracticeReal-time agent debugging with Langfuse tracesHow to debug a live agent incident using Langfuse traces. The search patterns, the 5-minute workflow, and the post-mortem that catches the root cause.ObservabilityAI AgentsProduction AI+2 moreRead Article8 min
AI Engineering in PracticeAgent cost optimization from trace dataHow to use Langfuse trace data to find where your agent burns tokens. The 4 queries, the cost-per-user view, and the 50 percent savings patterns.ObservabilityAI AgentsProduction AI+2 moreRead Article9 min
AI Engineering in PracticeLangfuse + Grafana: agentic AI monitoringHow to combine Langfuse traces with Grafana dashboards for agent monitoring. The integration, the panels, and the alerting that catches real problems.ObservabilityAI AgentsProduction AI+2 moreRead Article8 min