Evaluating LLM Responses

LLMs should be considered hallucinatory until proven otherwise! A lot of us have turned to augmenting LLMs with a knowledge store (such as Zilliz) to solve this problem. But this RAG setup can still face issues with hallucination. In particular - this can be caused from retrieving irrelevant context, not enough context, and more. TruLens is built to solve this problem. TruLens sits as the evaluation layer for the LLM stack, allowing you to shorten the feedback loop and iterate on your LLM app faster. We'll also talk about the different metrics you can use for evaluation and why you should consider LLM-based evals when building your app. Key Takeaways: - Learn about common failure modes for LLM apps - Learn the different evaluations that are useful for reducing hallucination, improving retrieval quality & more. - Learn about how to evaluate LLM apps with TruLens Link to Slides: https://bit.ly/46F0tNo Code along with us! https://bit.ly/3RiK9xr TruLens Documentation - https://www.trulens.org/trulens_eval/install/ TruLens GitHub - https://github.com/truera/trulens Those interested can also find the prompts used for LLM-based feedback functions in our open-source github repository here: https://github.com/truera/trulens/blob/main/trulens_eval/trulens_eval/feedback/prompts.py [SKILL TRACK] AI Fundamentals: https://bit.ly/40X8ZpU [COURSE] Working with the OpenAI API: https://bit.ly/48bN2pB [TUTORIAL] How to Build LLM Applications with LangChain: https://bit.ly/47XdseB [TUTORIAL] How to Train a LLM with PyTorch: https://bit.ly/3sM596r