Update README.md

Author: alopatenko

README.md

@@ -6,7 +6,7 @@
 The aim of this compendium is to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs. It does so by reviewing the top industry practices for assessing large language models (LLMs) and their applications. This work goes beyond merely cataloging benchmarks and evaluation studies; it encompasses a comprehensive overview of all effective and practical evaluation techniques, including those embedded within papers that primarily introduce new LLM methodologies and tasks. I plan to periodically update this survey with any noteworthy and shareable evaluation methods that I come across.
 I aim to create a resource that will enable anyone with queries—whether it's about evaluating a large language model (LLM) or an LLM application for specific tasks, determining the best methods to assess LLM effectiveness, or understanding how well an LLM performs in a particular domain—to easily find all the relevant information needed for these tasks. Additionally, I want to highlight various methods for evaluating the evaluation tasks themselves, to ensure that these evaluations align effectively with business or academic objectives.
 
-My view on LLM Evaluation: [Deck](LLMEvaluation.pdf), and  [video Analytics Vidhya](https://community.analyticsvidhya.com/c/datahour/evaluating-llms-and-llm-systems-pragmatic-approach) ([Data Phoenix Mar 5](https://www.youtube.com/watch?v=spgVnMgvLSw)) (by [Andrei Lopatenko](https://www.linkedin.com/in/lopatenko/))
+My view on LLM Evaluation: [Deck](LLMEvaluation.pdf), and [SF Big Analytics and AICamp](https://www.youtube.com/watch?v=dW89BHjLA4M) [video Analytics Vidhya](https://community.analyticsvidhya.com/c/datahour/evaluating-llms-and-llm-systems-pragmatic-approach) ([Data Phoenix Mar 5](https://www.youtube.com/watch?v=spgVnMgvLSw)) (by [Andrei Lopatenko](https://www.linkedin.com/in/lopatenko/))
 
 ![Evals are surprisingly often all you need](greg.png)