Fixing LLM Hallucinations with Retrieval Augmentation in LangChain #6
Large Language Models (LLMs) have a data freshness problem. Even some of the most powerful models, like ChatGPT's gpt-3.5-turbo and GPT-4, have no idea about recent events.
The world, according to LLMs, is frozen in time. They only know the world as it appeared through their training data.
So, how do we handle this problem? We can use retrieval augmentation. This technique allows us to retrieve relevant information from an external knowledge base and give that information to our LLM.
The external knowledge base is our "window" into the world beyond the LLM's training data. In this video, we…
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Chapters (10)
Hallucination in LLMs
1:32
Types of LLM Knowledge
3:08
Data Preprocessing with LangChain
9:54
Creating Embeddings with OpenAI's Ada 002
13:14
Creating the Pinecone Vector Database
16:57
Indexing Data into Our Database
20:27
Querying with LangChain
23:07
Generative Question-Answering with LangChain
25:27
Adding Citations to Generated Answers
28:42
Summary of Retrieval Augmentation in LangChain
Playlist
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