Grounded AI Models
Grounding in AI connects a model's outputs to real-world information, enhancing accuracy and reducing hallucinations. Utilizing retrieval augmented generation (RAG), models can access relevant external data, ensuring responses are factual and context-specific. This approach allows users to input their own data, making the model's functionality tailored and precise for their needs.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 Cohere co-founder Nick Frosst on building LLM apps for business?
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?
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?