RAG systems often fail in production due to issues like hallucination, making a robust evaluation pipeline essential before release.
A RAG system consists of retrieval and generation stages, which can fail independently and require separate measurement.
Manually creating test data is slow, expensive, and often lacks the volume and coverage needed for thorough RAG system evaluation.
The RAGAS framework builds a knowledge graph from documents to generate complex, multi-hop, and persona-driven questions for better test coverage.
This code walkthrough shows how to build a knowledge graph from documents, synthesize a test dataset, and visualize the graph structure.
Understand the core metrics for RAG evaluation, including retrieval metrics like context precision and recall, and generation metrics like faithfulness and relevancy.
Learn how to execute the evaluation pipeline using an LLM as a judge to score the RAG system's performance against the generated test set.
Synthetic data is a powerful bootstrap but has limitations like model bias, so it's crucial to involve human verification and version your test sets.
Evolve your evaluation strategy by starting with synthetic data, then progressively incorporating annotated real-world user queries to improve the test set.
Jobs that call for the skills explored in this talk.