Podcast Production with AI

🔄 Quick Recall: In the previous lesson, you learned recording fundamentals — microphone selection, room treatment, level management, and the garbage-in-garbage-out principle that makes source quality matter more than any AI enhancement. Now you’ll build a complete podcast production workflow that uses AI at every stage where it saves time without sacrificing quality.

The AI Podcast Production Pipeline

Here’s the complete workflow, from idea to published episode:

The time savings come from every stage except recording. Your voice and energy are irreplaceable — everything around them is acceleratable.

Stage 1: Planning with AI

Help me plan a podcast episode.

Podcast name: [name]
Format: [solo / interview / co-hosted]
Topic: [subject]
Target audience: [who listens]
Episode length: [minutes]

Create:
1. An episode outline with 3-5 segments
2. A hook for the first 30 seconds that grabs
   attention
3. Key talking points for each segment
4. 2-3 data points or examples that support
   the topic
5. A closing that encourages listener engagement

The key: AI generates the structure, you select and refine. Never let AI choose your topic or angle — that’s your editorial voice.

Stage 2: Text-Based Editing

Text-based editing is the single biggest productivity shift in podcast production. Instead of scrubbing through audio waveforms, you read a transcript and edit it like a document.

How it works in Descript:

1. Import your recording → automatic transcription
2. Read the transcript (10-15 minutes for a 45-minute episode)
3. Highlight filler words (“um,” “uh,” “you know”) → delete
4. Remove tangents by highlighting and deleting paragraphs
5. Rearrange segments by moving text blocks
6. The audio follows every edit automatically

What to cut:

• Filler words and verbal tics
• Long pauses (shorten to 0.5-1 second)
• Tangents that don’t serve the episode’s core message
• Repetitive points (keep the best version, cut the rest)
• Weak openings (start with the strongest 30 seconds)

✅ Quick Check: Why is text-based editing faster than traditional waveform editing? Because reading is faster than listening. You can scan a 45-minute transcript in 10-15 minutes, identify every section that needs cutting, and make all your edits in one pass. Traditional editing requires listening at 1x speed (or 1.5x at most), scrubbing back and forth to find precise cut points, and making one edit at a time. The speed difference is roughly 4-5x for a typical podcast episode.

Stage 3: AI Audio Enhancement

After editing, run your audio through AI enhancement:

Adobe Podcast Enhance Speech (free tier: 1 hour/day):

• Upload your edited audio
• One click: removes background noise, echo, and room reverb
• Downloads as a clean, enhanced file
• Quality: transforms home recordings to near-studio quality

Descript Studio Sound:

• Built into the editing workflow
• Toggle on for automatic noise removal and leveling
• Applies consistently across the entire episode

When enhancement isn’t enough: If the source audio has severe problems (heavy clipping, extreme echo, background music competing with speech), enhancement will improve but not fix them. This is why Lesson 3’s recording fundamentals matter — they set the quality ceiling that AI enhancement approaches.

Stage 4: Music and Sound Design

AI music generation eliminates the two biggest music pain points: licensing costs and library fatigue (hearing the same stock tracks everywhere).

Generating podcast music with Suno:

Create a 30-second podcast intro music track.

Style: [upbeat and professional / calm and thoughtful /
energetic and modern]
Instruments: [acoustic guitar / electronic beats /
piano / full band]
Mood: [confident / warm / exciting / contemplative]
Tempo: [slow / medium / fast]
Purpose: This is intro music for a podcast about
[topic]. It should feel [brand adjective] and
[brand adjective].

Include a natural fade-out in the last 5 seconds.

What you need:

• Intro music (15-30 seconds)
• Outro music (15-30 seconds)
• Transition stingers (3-5 seconds, for segment changes)
• Optional: subtle background music for specific segments

✅ Quick Check: Why generate custom podcast music instead of using royalty-free libraries? Three reasons: (1) Uniqueness — your intro won’t sound like 500 other podcasts using the same stock track. (2) Brand alignment — you can specify exactly the mood, tempo, and instruments that match your podcast’s personality. (3) No licensing anxiety — with a paid AI music plan, you have clear commercial rights. The tradeoff: AI-generated music is good but not yet at the level of a professional composer. For podcast intros and transitions, it’s more than sufficient.

Stage 5: Publishing Assets

AI generates the supporting content that turns a recording into a published episode:

Show notes: Paste your transcript and ask AI to generate a summary, key takeaways, timestamps, and mentioned resources.

Social media clips: Descript can automatically identify the most engaging 30-60 second segments and export them as audiograms or video clips for social promotion.

Transcripts: Automatic transcription makes your podcast accessible and SEO-discoverable. Every episode should publish a full transcript alongside the audio.

Key Takeaways

• AI-assisted podcast production cuts total production time from 8-14 hours to 2-4 hours per episode — the savings come from every stage except recording, where your authentic voice remains irreplaceable
• Text-based editing (Descript) is the single biggest time-saver: edit a 45-minute episode by reading and highlighting a transcript in 10-15 minutes instead of scrubbing through waveforms for 2-4 hours
• The optimal AI workflow uses AI strategically — research and outlining, text-based editing, audio enhancement, music generation, and publishing assets — while keeping human judgment for topic selection, recording, and editorial voice
• AI-generated podcast music eliminates licensing costs and library fatigue while creating unique audio branding — specify mood, tempo, instruments, and brand personality for custom results
• Every episode should publish with AI-generated show notes, timestamps, social clips, and a full transcript for accessibility and SEO

Up Next: You’ll go beyond stock voices into voice cloning — creating a custom AI version of your own voice (or a character voice) that can narrate content, generate variations, and scale your audio production capacity.