With the rapid pace of AI, developers are often faced with a paradox of choice: how to choose the right prompt, how to trade-off LLM quality vs cost? Evaluations can accelerate development with structured process for making these decisions. But, we've heard that it is challenging to get started. So, we are launching a series of short videos focused on explaining how to perform evaluations using LangSmith.
This video focuses on RAG (Retrieval Augmented Generation). We show you how to evaluate whether your output matches a ground truth reference answer. You can use LangSmith to create a dataset of answers you expect, run an evaluation, and dive into output traces - helping you catch inaccuracies in your responses.
Documentation:
docs.smith.langchain.com/cook...
Негізгі бет RAG Evaluation (Answer Correctness) | LangSmith Evaluations - Part 12
Пікірлер: 6