Retrieval-Augmented Generation (RAG) demo with canopy

Introducing RAG: Reducing AI Hallucinations & Keeping Conversations Contextual
Large language models like GPT can hold fluent conversations. But they sometimes "hallucinate" by making up information.
RAG allows injecting external knowledge to reduce hallucinations. It exposes data to models so they can answer questions specific to that data.
RAG keeps conversations grounded in the knowledge base. Without RAG, models may not understand the context. RAG maintains context across questions.
Demo shows RAG answering very niche questions based on papers loaded in the knowledge base. Without RAG, model doesn't know details from those specific papers.
Benefits include reducing hallucinations by citing sources. General models can't identify where info is from. RAG can reference sources to avoid fabricating information.
Use cases: If you have niche external data and want AI to hold contextual conversations about it, RAG is very valuable. It allows AI to "talk to" your data.
RAG allows search technology to now handle multimodal conversations instead of just text search. You can talk to your data rather than just retrieve it.
We will cover RAG more in depth in future episodes. Also fine-tuning differences. Stay tuned as we track evolutions in how AI researchers use RAG over time.
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