LLM Testing
Retrieval-Augmented Generation (RAG) pipelines are notoriously hard to evaluate. A model can produce fluent, confident-sounding answers that are completely unsupported by the retrieved documents. RAGAS gives you a structured framework to measure exactly that — and to wire it into your CI pipeline so regressions surface before they reach
Testing
RAG (Retrieval-Augmented Generation) pipelines combine a retrieval system with an LLM to answer questions using your own data. Testing RAG pipelines is challenging because both the retrieval component and the generation component can fail independently — or fail together in ways that aren't obvious. This guide covers testing
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