Contextual Learning Insights
Context learning is revolutionizing how models process information, with recent advancements allowing for context lengths of up to 100,000 tokens. This enables the retrieval augmented generation technique, which empowers companies to efficiently answer questions based on their knowledge bases by chunking documents and converting them into meaningful embeddings. As the landscape evolves, the steps to implement these solutions are expected to simplify, addressing customer pain points more effectively.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
The Enterprise LLM Landscape with Atul Deo - 640
Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode Vector Databases and the Power of RAG and the clip Evolution of AI, as well as in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256 and the clip Evaluating LLM Responses?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data in the episode Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - 672 and the clip Instruction Tuning Insights?