@_vmlops: RAG Evaluation & Testing in Production (Offline + Online) Interview Preparation Playbook for Model Evaluators, Al QA & …
Summary
A shared resource linking to an interview preparation playbook focused on RAG evaluation and testing for LLMs.
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EnterpriseRAG: Benchmarking LLM Instruction Adherence and Robustness under Non-Ideal Enterprise Retrieval
Introduces EnterpriseRAG, a benchmark of 983 expert-validated samples across six domains that evaluates LLM instruction adherence and robustness under non-ideal enterprise retrieval conditions including noise, knowledge gaps, and factual conflicts. Evaluation of 13 LLMs reveals a large gap between per-constraint satisfaction and holistic compliance, highlighting the need for context-aware protocols in production RAG systems.
Evaluating RAG Metrics in Applied Contexts: An Experiment, Its Findings and Its Limitations
This paper presents an empirical study evaluating RAG evaluation metrics from four libraries (Ragas, DeepEval, RAGChecker, Opik) by comparing them to human judgments and standard recall metrics, using a question-answering dataset created from business data.
@shrav_10: Interviewed another candidate today. I asked him what RAG is. He replied: RAG is a technique that allows an LLM to answ…
A hiring manager shares a candidate's correct definitions of RAG and fine-tuning, then asks followers to explain when to use one over the other.
Eval-Pair Matrix: Answer-Paired Meta-Evaluation of LLM Judges for Grounded RAG
Introduces Eval-Pair Matrix, a controlled meta-evaluation protocol for source-grounded RAG that induces hidden contradictions to detect self-leniency in LLM judges. The study finds minimal same-model effects and emphasizes methodological improvements for RAG judge studies.
@DanKornas: LLM eval is where most AI demos start becoming real systems. LLM-Evaluation is a public GitHub resource with workshop s…
A tweet announces LLM-Evaluation, a public GitHub repository containing workshop slides, sample notebooks, prompts, and reference links for evaluating LLMs, generative AI, and RAG systems, aiming to provide a practical map of evaluation workflows.