Build AI Chatbot Training Data from Expert Interviews

Monitor RSS feeds from AI-focused podcasts (Lex Fridman, AI Podcast, etc.). Automatically download new episodes featuring AI executives, researchers, or breakthrough announcements to cloud storage.

Process audio files through OpenAI's Whisper API to generate accurate transcriptions. Configure for speaker diarization to identify who said what, and timestamp important segments.

Parse transcripts to identify: key concepts, technical explanations, predictions, methodologies, and expert opinions. Structure output as Q&A pairs, fact statements, and reasoning chains suitable for training data.

Cross-reference extracted information for accuracy, identify potential biases or speculation, and flag statements that need verification. Generate confidence scores for each piece of information.

Convert structured knowledge into vector embeddings for similarity search and retrieval. Organize by topic clusters (AGI, computer vision, NLP, etc.) with metadata tags for easy filtering.

Store processed knowledge in a structured database with fields for: source, confidence score, topic tags, validation status, and usage rights. Create views for different training purposes and export formats.