Optimizing AI Models
AI engineers are increasingly focusing on optimizing models not just during training but also at inference time. Techniques like RAG (Retrieval-Augmented Generation) enhance model responses by retrieving relevant information from databases, allowing for more precise answers. The discussion highlights the evolution from traditional fine-tuning methods to innovative approaches that leverage multi-shot prompting and hierarchical RAG queries for improved accuracy.In this clip
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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 Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip Evaluating RAG Systems?
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 Building LLM-Based Applications with Azure OpenAI with Jay Emery - 657 and the episode with Cohere co-founder Nick Frosst on building LLM apps for business?