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Podcast Maker Journey - Developers

Use this journey to integrate Podcast Maker into repeatable, testable pipelines for scripted audio generation and distribution.

Overview

Entry Conditions

  • Inputs: API credentials, topic payload schema, content constraints, output destination.
  • Skill level: Intermediate to advanced (API and workflow automation).
  • Expected time: 60-120 minutes for first implementation.

Success Target

Automate one full podcast generation path from prompt to exported artifact with predictable quality.

Setup

  • Duration: 10-20 minutes (configurable per template)
  • Speakers: 1-2 synthetic speakers
  • Voice style: Neutral/professional with stable pacing
  • Research provider: Perplexity (structured fact gathering for scripted outputs)

Pre-Production Checklist

  1. Define request schema for analysis/research/script/render/export stages.
  2. Store provider credentials via environment variables.
  3. Configure retry/error policy for external research and render calls.
  4. Add logging for prompt versions and output hashes.

Production

Podcast Maker Workflow

  1. Analysis
  2. Validate input payload and enforce required fields.
  3. Derive episode objective and section plan programmatically.
  4. Research
  5. Fetch source context with provider abstraction.
  6. Normalize citations and drop low-confidence results.
  7. Script
  8. Generate structured script JSON (intro/segments/outro/CTA).
  9. Run lint-style checks for length and forbidden terms.
  10. Render
  11. Render audio using configured speaker profile.
  12. Execute post-render QA hooks (duration, loudness, clipping checks).
  13. Export
  14. Persist artifact + metadata to storage.
  15. Trigger downstream publish/webhook integration.

Optimization

Success Criteria

  • End-to-end pipeline completes without manual intervention.
  • Output passes automated quality checks.
  • Metadata includes provenance for research and prompt version.
  • Failure paths are observable with actionable logs.

Checkpoints

  • Before render: Unit/integration checks pass for script payload.
  • After render: Verify duration bounds and transcript alignment.
  • After publish: Monitor error rate, latency, and output quality metrics.

Troubleshooting

Common Issues and Fixes

  • Provider timeouts: Add retries with exponential backoff and fallback provider.
  • Inconsistent scripts: Pin model settings and enforce schema validation.
  • Audio quality failures: Add deterministic render settings and QA thresholds.
  • Broken exports: Validate storage credentials and file naming conventions.
  • Debug difficulty: Log stage-level inputs/outputs with correlation IDs.

Next step: integrate this into Advanced Usage automation patterns.