LLM EngineeringQuery anonymization for RAG bias mitigationHow to strip names, roles, and demographics from queries before retrieval to reduce RAG bias. The redaction pipeline and the 3 leakage traps to avoid.RAGGuardrailsData Processing+2 moreRead Article9 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 EngineeringPydantic output structuring for RAG agent plansHow to use Pydantic models to force your RAG planner LLM to return structured steps. The schema, the retry loop, and why plain JSON prompts break in production.RAGPydanticStructured Output+2 moreRead Article8 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 EngineeringQuery rewriting in RAG with LLMs: the rewrite loopHow LLM-powered query rewriting fixes vague user questions before retrieval. The prompt, the multi-query fan-out, and when rewriting hurts more than helps.RAGLLMPrompt Engineering+2 moreRead Article8 min
LLM EngineeringLLM-based content filtering for RAG pipelinesHow to filter irrelevant retrieved chunks with a cheap LLM call before the final answer. The prompt, the batch pattern, and the 40 percent noise reduction.RAGLLMData Processing+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