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1
Why GPT-4 can fail - hallucinations
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What we can do with retrieval augmentation
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How retrieval augmentation works
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Scraping docs for LLMs
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Preprocessing and chunking text for GPT4
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Creating embeddings with text-embedding-ada-002
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Creating the Pinecone vector database
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Retrieving relevant docs with semantic search
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GPT-4 generated answers
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GPT-4 with augmentation vs. GPT-4 without
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Building powerful tools is almost too easy
Description:
Learn how to enhance GPT-4's capabilities and overcome its limitations through retrieval augmentation in this 27-minute video tutorial. Explore techniques to address hallucinations and outdated information in Large Language Models by combining OpenAI's ChatCompletion endpoint with the Pinecone vector database. Follow along as the process of augmenting GPT-4's knowledge is demonstrated using the LangChain Python library as an example. Discover methods for scraping documentation, preprocessing text, creating embeddings, and performing semantic searches to retrieve relevant information. Compare the performance of GPT-4 with and without augmentation, and gain insights into building powerful AI tools with ease.

GPT 4 - Superpower Results With Search

James Briggs
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