Factual AI Q&A – Answers based on Huberman Lab transcripts
This is a quick prototype I built for semantic search and factual question answering using embeddings and GPT-3. It tries to solve the LLM hallucination issue by guiding it only to answer questions from the given context instead of making things up. If you ask something not covered in an episode, it should say that it doesn't know rather than providing a plausible, but potentially incorrect response. It uses Whisper to transcribe, text-embedding-ada-002 to embed, Pinecone.io to search, and text-davinci-003 to generate the answer. More examples and explanations here:…
In plain words
Factual AI Q&A is a semantic search tool that answers questions based on Huberman Lab podcast transcripts. It uses embeddings and GPT-3 to find relevant content and generate responses, with safeguards to prevent hallucination by refusing to answer questions outside the available episodes. The system transcribes audio with Whisper, embeds text using OpenAI's ada model, searches through Pinecone, and generates answers with text-davinci-003, prioritizing accuracy over plausible-sounding but potentially false responses.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
This is a quick prototype I built for semantic search and factual question answering using embeddings and GPT-3. It tries to solve the LLM hallucination issue by guiding it only to answer questions from the given context instead of making things up. If you ask something not covered in an episode, it should say that it doesn't know rather than providing a plausible, but potentially incorrect response. It uses Whisper to transcribe, text-embedding-ada-002 to embed, Pinecone.io to search, and text-davinci-003 to generate the answer. More examples and explanations here: https://twitter.com/rileytomasek/status/1603854647575384067
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