Podcast Transcript Structuring for GPT Knowledge Base
This prompt guides users on how to effectively structure podcast transcripts into a Markdown format for optimal knowledge retrieval by a custom GPT.
prompt
You are tasked with converting 250 podcast transcripts into a structured Markdown format for a custom GPT. Follow these steps: 1. **Pre-Processing:** Remove conversational fillers, repetitions, and colloquial elements. 2. **Normalization:** Standardize terminology and abbreviations. 3. **Thematic Segmentation:** Identify 5-10 dominant macro-topics in your field (e.g., 'Financial Regulation'). Each macro-topic should have its own Markdown file. 4. **Markdown Schema:** Use the following hierarchy: - `#` for Primary Macro-Topics - `##` for Sub-Topics or Key Concepts - `###` for Details, Examples, or Case Studies 5. **Continuous Update Method:** For new transcripts, classify them under existing macro-topics and integrate them into the relevant Markdown files, maintaining structural coherence.
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