RAG
RAG (Retrieval-Augmented Generation) systems fail in ways that are invisible without the right tests. The LLM might generate confident-sounding output that's completely ungrounded in the retrieved documents. The retriever might fetch irrelevant documents. The response might be faithful to the retrieved context but that context might
Testing
Retrieval-Augmented Generation (RAG) pipelines are complex systems with multiple failure points: chunking can destroy context, retrieval can miss relevant documents, and the LLM can hallucinate facts not present in the retrieved chunks. Testing a RAG system requires evaluating each stage and the full pipeline together. This guide covers how
LLM Testing
Retrieval-Augmented Generation (RAG) pipelines are notoriously hard to evaluate.
Testing
RAG (Retrieval-Augmented Generation) pipelines combine a retrieval system with an LLM to answer questions using your own data.
AI Testing
LlamaIndex is a popular framework for building RAG (Retrieval-Augmented Generation) pipelines. Testing these pipelines requires verifying three distinct components: the retriever (is it finding relevant documents?), the context (is the retrieved content accurate?), and the generator (is the answer faithful to the context and free of hallucination?). This guide
Testing
TruLens is an open-source observability and evaluation framework for LLM applications. Its core contribution is the RAG Triad — three metrics (groundedness, context relevance, answer relevance) that together diagnose where a RAG pipeline breaks. Traces are stored locally or remotely, and a built-in dashboard makes debugging fast. What TruLens
Testing
Ragas gives you a rigorous metric suite for RAG pipelines: faithfulness, answer relevancy, context precision, and context recall. Each metric isolates a different failure mode — bad retrieval vs. bad generation vs. incomplete recall. This guide shows you how to compute them, interpret scores, and integrate Ragas into CI. The RAG