Transcript Processing Workflow: 5 AI Prompts for Clean Results

Raw transcripts are messy. AI transcription gives you accurate words, but you get "um", "uh", incomplete sentences, and "Speaker A" instead of names. Before you can use that transcript for anything—blog posts, meeting notes, research analysis—you need to process it.

This guide gives you a complete workflow with copy-paste AI prompts for each step. The prompts work with ChatGPT, Claude, or any AI assistant.

Quick Navigation

• The 5-Step Processing Workflow
• Step 1: Transcript Cleaning
• Step 2: Speaker Labeling
• Step 3: Timestamp Optimization
• Step 4: Section Organization
• Step 5: Content Repurposing
• Processing Checklist
• Workflow Tips from Experience

The 5-Step Processing Workflow

Processing time varies by transcript length and AI tool used.

Not every transcript needs every step. A quick internal meeting might only need cleaning. A podcast going to YouTube needs all five. Use what you need.

Step 1: Transcript Cleaning

The foundation step. Every transcript benefits from cleaning—removing verbal filler, fixing run-on sentences, and making text readable.

AI Prompt: Transcript Cleaner

Clean this raw transcript while preserving the speaker's voice and all important content.

CLEANING RULES:
1. Remove filler words: um, uh, like (when filler), you know, I mean, basically, actually, honestly, literally (when not literal)
2. Remove false starts: "I was going to—I decided to" → "I decided to"
3. Remove repetitions: "It was really, really good" → "It was really good"
4. Fix incomplete sentences when meaning is clear
5. Preserve intentional emphasis and speaking style
6. Keep technical terms exactly as spoken
7. Maintain all factual content—don't summarize or omit

DO NOT:
- Remove emotional language or emphasis
- Change meaning or intent
- Add information not present
- Over-formalize casual speech
- Remove speaker personality

OUTPUT FORMAT:
- Return the full cleaned transcript
- Use paragraph breaks at natural pauses or topic shifts
- Preserve speaker labels exactly as they appear

TRANSCRIPT TO CLEAN:
[PASTE YOUR TRANSCRIPT HERE]

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Prompt by BrassTranscripts (brasstranscripts.com)
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Before/After Example

Before (raw):

Speaker A: So, um, I was thinking that we should, you know, probably look at the, the quarterly numbers because, I mean, they're actually really important for, like, understanding where we're at.

After (cleaned):

Speaker A: I was thinking we should look at the quarterly numbers because they're really important for understanding where we're at.

Same meaning, same speaker voice, half the words.

Step 2: Speaker Labeling

Replace generic labels with actual names. This step transforms a transcript from confusing to usable.

AI Prompt: Speaker Labeler

Replace speaker labels in this transcript with the correct names based on the context I provide.

SPEAKER INFORMATION:
- Speaker A is: [NAME AND ROLE]
- Speaker B is: [NAME AND ROLE]
- Speaker C is: [NAME AND ROLE]
(Add more as needed)

IF SPEAKERS AREN'T IDENTIFIED:
- Analyze speaking patterns to suggest likely roles
- Note: "Based on question patterns, Speaker A appears to be the interviewer"
- Label by role if names unknown: "Interviewer:", "Guest:", "Host:"

LABELING RULES:
1. Replace ALL instances of "Speaker A/B/C" with provided names
2. Use consistent format: "Name:" at start of each speaking turn
3. Preserve all original content exactly
4. Note any speakers who couldn't be confidently identified

OUTPUT FORMAT:
- Full transcript with corrected speaker labels
- If any speakers unclear, add note at the top explaining assumptions

TRANSCRIPT TO LABEL:
[PASTE YOUR CLEANED TRANSCRIPT HERE]

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Prompt by BrassTranscripts (brasstranscripts.com)
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Tip: When You Don't Know Names

For interviews or calls where you don't have speaker names, the AI can often identify roles:

• Interviewers ask more questions
• Hosts do introductions and transitions
• Guests tell stories and share expertise

Provide context: "This is a podcast interview about AI. One speaker is the host, one is a guest expert in machine learning."

Step 3: Timestamp Optimization

Different outputs need different timestamp formats. YouTube captions need SRT format. Reading transcripts need paragraph timestamps. Research analysis needs precise timing.

AI Prompt: Timestamp Formatter

Reformat the timestamps in this transcript for my specific use case.

MY USE CASE: [CHOOSE ONE]
- READING: Add timestamps every 1-2 minutes as paragraph markers
- VIDEO CAPTIONS: Format as SRT/VTT with 2-3 second segments
- RESEARCH: Preserve all timestamps, format as [HH:MM:SS]
- PODCAST SHOW NOTES: Keep only major topic timestamps
- REMOVE: Strip all timestamps for clean reading

FORMATTING RULES BY USE CASE:

For READING:
- Add timestamp at start of each major topic or every 2 minutes
- Format: [12:34] at paragraph start
- Remove mid-sentence timestamps

For VIDEO CAPTIONS:
- Keep segments under 42 characters wide
- 2-3 second display time per segment
- Match natural speech breaks

For RESEARCH:
- Preserve all original timestamps
- Standardize format: [HH:MM:SS]
- Align with speaker turns

For PODCAST SHOW NOTES:
- Extract only topic-change timestamps
- Format: [MM:SS] Topic Name
- Create clickable chapter markers

TRANSCRIPT TO REFORMAT:
[PASTE YOUR TRANSCRIPT HERE]

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Prompt by BrassTranscripts (brasstranscripts.com)
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Step 4: Section Organization

Add structure to make long transcripts navigable. This is essential for transcripts over 15 minutes.

AI Prompt: Transcript Section Organizer

Add section headers and structure to this transcript based on topic changes.

ORGANIZATION RULES:
1. Identify natural topic shifts in the conversation
2. Add clear H2 headers for major sections
3. Add H3 subheaders for subtopics within sections
4. Create a table of contents at the top
5. Preserve all original content—don't summarize

HEADER STYLE: [CHOOSE ONE]
- DESCRIPTIVE: "Discussion of Q3 Revenue Performance"
- QUESTION-BASED: "How Did Q3 Revenue Perform?"
- TOPIC-BASED: "Q3 Revenue"
- TIMESTAMP-BASED: "[12:34] Q3 Revenue Discussion"

ADDITIONAL FORMATTING:
- [ ] Add bold to key decisions or action items
- [ ] Add bullet summaries at end of each section
- [ ] Highlight direct quotes worth noting
- [ ] Mark follow-up items or unresolved questions

OUTPUT STRUCTURE:
## Table of Contents
[Auto-generated list of sections]

## Section 1: [Header]
[Content]

## Section 2: [Header]
[Content]

TRANSCRIPT TO ORGANIZE:
[PASTE YOUR TRANSCRIPT HERE]

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Prompt by BrassTranscripts (brasstranscripts.com)
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Step 5: Content Repurposing

The final step transforms your cleaned, labeled, organized transcript into something new—a blog post, executive summary, social content, or show notes.

AI Prompt: Transcript Content Repurposer

Transform this processed transcript into a specific content format.

OUTPUT FORMAT: [CHOOSE ONE]
- BLOG POST: 800-1,500 word article with introduction, key points, conclusion
- EXECUTIVE SUMMARY: 1-page overview with key decisions and action items
- MEETING NOTES: Structured notes with attendees, topics, decisions, next steps
- SHOW NOTES: Episode description, timestamps, key quotes, resources mentioned
- SOCIAL MEDIA: 5-10 posts highlighting key insights (Twitter/LinkedIn format)
- NEWSLETTER: Email-friendly summary with one main takeaway
- VIDEO DESCRIPTION: YouTube description with timestamps and keywords

CONTENT GUIDELINES:
1. Preserve accuracy—don't add information not in the transcript
2. Use direct quotes for compelling statements
3. Include attribution: "According to [Speaker]..."
4. Maintain the tone of the original conversation
5. Highlight actionable insights over general discussion

ADDITIONAL REQUESTS:
- [ ] Include 3-5 pull quotes for social sharing
- [ ] Add SEO keywords for blog optimization
- [ ] Create headline options (3-5 variations)
- [ ] Suggest related topics for follow-up content

TRANSCRIPT TO REPURPOSE:
[PASTE YOUR PROCESSED TRANSCRIPT HERE]

CONTEXT:
- Original content type: [PODCAST/MEETING/INTERVIEW/WEBINAR]
- Target audience: [DESCRIBE]
- Publication channel: [BLOG/NEWSLETTER/SOCIAL/INTERNAL]

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Prompt by BrassTranscripts (brasstranscripts.com)
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Processing Checklist

Use this checklist to track your processing steps. Copy it for each transcript you process.

## Transcript Processing Checklist

**File**: ____________________
**Date**: ____________________
**Length**: ______ minutes
**Speakers**: ________________

### Pre-Processing
- [ ] Raw transcript downloaded from transcription service
- [ ] Audio quality verified (no missing sections)
- [ ] Speaker count confirmed

### Step 1: Cleaning
- [ ] Filler words removed
- [ ] False starts cleaned
- [ ] Grammar corrected where needed
- [ ] Readability verified

### Step 2: Speaker Labeling
- [ ] All speakers identified
- [ ] Names/roles assigned consistently
- [ ] Uncertain attributions noted

### Step 3: Timestamps (if needed)
- [ ] Format chosen for use case
- [ ] Timestamps standardized
- [ ] Topic markers added

### Step 4: Organization (if needed)
- [ ] Section headers added
- [ ] Table of contents created
- [ ] Key points highlighted

### Step 5: Repurposing (if needed)
- [ ] Target format selected
- [ ] Content generated
- [ ] Accuracy verified against source

### Final Quality Check
- [ ] All speaker names spelled correctly
- [ ] No content accidentally removed
- [ ] Format matches intended use
- [ ] File saved in correct location

Workflow Tips from Experience

Start with the end in mind. Before processing, know what you're creating. A podcast going to YouTube needs all five steps. Internal meeting notes might only need cleaning and speaker labels. Don't over-process.

Batch similar transcripts. If you're processing multiple podcast episodes, run Step 1 on all of them, then Step 2 on all, etc. You'll get faster as you repeat the same prompt type.

Keep the raw transcript. Always save your original before processing. If something goes wrong or you need different formatting later, you want the source material.

Process in order. The prompts build on each other. Cleaning before labeling means cleaner speaker attribution. Organizing before repurposing means better structure in your final content.

Verify facts, not just formatting. AI processing can occasionally change meaning. Spot-check key quotes and numbers against your original transcript.

Frequently Asked Questions

How long does transcript processing take?

Processing time varies by transcript length and complexity. Most users complete cleaning and speaker labeling quickly, while content repurposing takes longer because you're generating new content. Actual time depends on your AI tool and transcript complexity.

Which AI tool works best for transcript processing?

ChatGPT-4, Claude, and Gemini all handle transcript processing well. For long transcripts, check each tool's current context window limits. The prompts in this guide work with any of them.

Should I process the whole transcript or sections?

For transcripts under 5,000 words, process the whole thing at once. For longer transcripts, break into logical sections (by topic or speaker turn) to avoid context limits and get more consistent results.

How do I handle multiple speakers without names?

Use the Speaker Labeling prompt to identify speakers by role or speaking style. If speakers aren't distinguishable, label by order of appearance (Speaker 1, Speaker 2) and note any identifying context (e.g., "Speaker 1 appears to be the interviewer based on question patterns").

Related Resources

• AI Prompt Guide — 121 specialized prompts for transcript analysis
• Transcript Format Guide — Choose the right output format (TXT, SRT, VTT, JSON)
• Transcription Pricing Calculator — Calculate your transcription costs

Ready to process transcripts? Upload your audio to BrassTranscripts and get your raw transcript with speaker identification—then use these prompts to create exactly what you need.