Why Evals Matter | LangSmith Evaluations - Part 1

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 lays out 4 main considerations for evaluation: (1) dataset, (2) evaluator, (3) task, (4) how to apply evaluation to improve your product (e.g., unit tests, A/B tests, etc).
Getting started documentation:
docs.smith.langchain.com/eval...

[@chaitanyagoel9837]

🎯 Key points for quick navigation:
00:00 *🎥 Introduction to Evaluations*
- Introduction to the importance of evaluations for new models.
- Overview of public evaluations and the components involved.
00:54 *🧪 Evaluation Methods*
- Explanation of human evaluations and their structure.
- Comparative evaluation methods like Chatbot Arena.
- Different metrics used to interpret results, such as ELO scores.
02:44 *🔍 Personalized Testing*
- Discussion on the trend of personalized testing and evaluations.
- Methods to build and curate datasets for evaluations.
- Examples of user interactions and synthetic data generation.
04:05 *🤖 Evaluation Judges*
- Various types of judges for evaluations including humans and LLMs.
- Modes of evaluation, both reference-free and ground-truth based.
- Application of evaluations in different contexts like unit tests and AB testing.
05:28 *🔧 Implementing Evaluations with LangSmith*
- Introduction to LangSmith platform for running evaluations.
- Overview of LangSmith features: dataset creation, evaluator definition, trace inspections.
- Future videos will explore detailed steps to build evaluations using LangSmith.
Made with HARPA AI

[@andrianantenainaprincyraso7162]

cool !

[@aaronbiliyok4553]

Hey Lance Good job... can you please share you slides?

[@kareammohamad]

Fine