@dwarkesh_sp - You are an expert transcript editor. Your task is to

Dwarkesh Patel
Dwarkesh Patel@dwarkesh_sp
Was working on a script to get Gemini to improve autogenerated transcripts. Gave Gemini the original audio too in order to identify mistakes. Kept turning out shitty, until suddenly the transcript became absolutely perfect and human quality. Finally figured out what changed... I accidentally forgot to include the "first draft" autogenerated transcript in Gemini's prompt.
Dwarkesh Patel
Dwarkesh Patel@dwarkesh_sp
Ah actually it's a bit hit or miss without the autogenerated transcript. Maybe a better prompt might make it work tho. Definitely does an amazing job of refining an autogenerated transcript if prompted correctly.
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Dwarkesh Patel
Dwarkesh Patel@dwarkesh_sp
2024-12-22
You are an expert transcript editor. Your task is to enhance this transcript for maximum readability while maintaining the core message. IMPORTANT: Respond ONLY with the enhanced transcript. Do not include any explanations, headers, or phrases like "Here is the transcript." Note: Below you'll find an auto-generated transcript that may help with speaker identification, but focus on creating your own high-quality transcript from the audio. Please: 1. Fix speaker attribution errors, especially at segment boundaries. Watch for incomplete thoughts that were likely from the previous speaker. 2. Optimize for readability over verbatim accuracy: - Remove filler words (um, uh, like, you know) - Eliminate false starts and repetitions - Convert rambling sentences into clear, concise statements - Break up run-on sentences into shorter ones - Maintain natural conversation flow while improving clarity 3. Format the output consistently: - Keep the "Speaker X 00:00:00" format (no brackets, no other formatting) - Add TWO line breaks between speaker/timestamp and the text - Use proper punctuation and capitalization - Add paragraph breaks for topic changes - When you add paragraph breaks between the same speaker's remarks, no need to restate the speaker attribution - Preserve distinct speaker turns Example input: Speaker 1 00:01:15 Um, yeah, so like, what I was thinking was, you know, when we look at the data, the data shows us that, uh, there's this pattern, this pattern that keeps coming up again and again in the results. Example output: Speaker 1 00:01:15 When we look at the data, we see a consistent pattern in the results. And when we examine the second part of the analysis, it reveals a completely different finding. Enhance the following transcript, starting directly with the speaker format:

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