Standard Operating Procedure: Video Transcription & Summarization Workflow
Defines an end-to-end workflow for ingesting video, generating transcripts, producing summaries, and delivering outputs to stakeholders.
What this file does
Defines an end-to-end workflow for ingesting video, generating transcripts, producing summaries, and delivering outputs to stakeholders.
When to use it
- You need a repeatable process for video transcription and summarization
- You are building or documenting a pipeline that involves speech-to-text and LLM summarization
- You want to formalize roles, quality checks, and exception handling for media processing
- You are onboarding operators or engineers to a transcription service
Standard Operating Procedure: Video Transcription & Summarization Workflow
1. Purpose
This SOP defines the end-to-end workflow for accepting video assets, generating transcripts, producing summaries, and delivering outputs to stakeholders. It ensures consistent quality, predictable turnaround times, and clear accountability as the platform evolves.
2. Scope
- Applies to all environments (local development, staging, production) used to process customer video content.
- Covers manual and automated execution of the workflow, along with exception handling and quality checks.
- Excludes product feature prioritization, model research spikes, or vendor contract negotiations.
3. Roles and Responsibilities
- Product Owner (PO): Prioritizes intake requests, defines acceptance criteria, and signs off on release readiness.
- Operations Lead: Oversees daily processing queue, assigns operators, and approves reruns.
- Transcription Operator: Runs ingestion, monitors transcription jobs, and completes transcript QA.
- Summarization Operator: Tunes summary prompts/templates, validates outputs, and packages deliverables.
- Platform Engineer: Maintains infrastructure, resolves system incidents, and manages deployments.
4. Definitions
- Raw Video Asset: Original media file uploaded by customers or ingested from external sources.
- Processing Job: A unit of work that encapsulates transcription and summarization tasks for a single asset.
- Confidence Score: Numeric indicator returned by the speech-to-text engine showing transcript reliability.
- Delivery Package: Bundle of final artifacts (transcript, summaries, metadata) made available to consumers.
5. Prerequisites
- Valid credentials for storage, speech-to-text provider, and summarization LLM endpoints.
- Approved version of the platform infrastructure deployed and smoke-tested.
- Current prompt templates and summary formats stored under
docs/prompts/(create if absent). - Access to monitoring dashboards and alerting channels (Slack
#transcription-ops). - Knowledge base links for vendor-specific quotas, rate limits, and maintenance windows.
6. Standard Workflow
- Intake & Triage
- Receive request via intake form or API.
- Confirm media format, duration, language, and required turnaround.
- Create or update a processing job record in the work tracker (e.g., Jira, Linear), tagging
transcriptionandsummarycomponents.
- Asset Staging
- Upload or sync video to object storage bucket following naming convention:
videos/{yyyy}/{mm}/{request-id}/source.ext. - Generate a checksum and log it in the job record.
- Trigger antivirus/media validation if enabled. Quarantine and escalate on failure.
- Upload or sync video to object storage bucket following naming convention:
- Transcription Execution
- Submit audio to the configured speech-to-text engine with language and diarization options.
- Monitor queue length and job status via dashboard; investigate any job exceeding SLA thresholds.
- On completion, store transcript as Markdown or JSON under
data/transcripts/{request-id}.jsonwith metadata (timestamps, speaker tags, confidence scores).
- Transcript Quality Assurance
- Review flagged segments (confidence score < 0.85) and correct misheard phrases using source audio.
- Validate speaker labeling, punctuation, and formatting.
- Record QA results in the job record, noting any sections requiring follow-up.
- Summarization Execution
- Select summary templates (executive summary, bullet highlights, action items) based on request.
- Run summarization service with transcript input; capture model parameters and prompt version in logs.
- Store outputs under
data/summaries/{request-id}/with clear filenames (executive.md,bullets.md, etc.).
- Summary Quality Assurance
- Verify factual consistency against transcript; ensure no hallucinated names, dates, or figures.
- Check tone and length meet acceptance criteria; adjust prompt or regenerate if needed.
- Confirm redaction of sensitive data where required.
- Delivery & Handoff
- Compile delivery package (transcript, summaries, metadata report).
- Notify stakeholders through the agreed channel (email, Slack, API callback) and attach delivery link.
- Update job record to "Complete" with timestamps, processing duration, and final storage location.
7. Exception Handling
- Transcription Failure: Retry once with alternate engine parameters; escalate to Platform Engineer if second attempt fails.
- Low Confidence Transcript: Flag to Operations Lead; perform manual review or request customer clarification.
- Summarization Drift: Roll back to last known good prompt template and open a ticket for prompt tuning.
- Storage Outage: Pause new ingests, switch to secondary bucket if available, and follow incident protocol (Section 9).
8. Automation & Tooling Guidelines
- Maintain IaC definitions (e.g., Terraform) for storage buckets, queues, and compute resources.
- Version control prompt templates and summarization configurations alongside application code.
- Configure CI to run smoke tests for transcription and summarization services before deployment.
- Implement alerts for queue backlog, API error rates, and cost anomalies.
9. Incident Management
- Declare an incident if SLAs are at risk for more than 30 minutes or critical functionality is down.
- Platform Engineer initiates incident bridge and posts updates every 30 minutes.
- Record timeline, root cause, and remediation actions in the incident report template under
docs/incidents/. - After resolution, conduct a blameless postmortem and log follow-up tasks.
10. Change Management
- Proposals for workflow changes must include impact analysis, rollout plan, and rollback steps.
- Obtain sign-off from Product Owner and Platform Engineer prior to production changes.
- Update this SOP, related runbooks, and onboard training materials when changes ship.
- Communicate upcoming changes to operators at least 48 hours in advance.
11. Metrics & Continual Improvement
- Track turnaround time, transcript accuracy, summary satisfaction score, and incident rate.
- Review metrics in monthly ops retrospective; identify automation or training opportunities.
- Pilot improvements in staging before production adoption and document learnings.
12. Document Control
- Owner: Operations Lead
- Review Cadence: Quarterly or after major workflow update (whichever comes first)
- Revision History:
Version Date Author Notes 0.1 2024-06-02 Operations Lead TBD Initial draft covering intake-to-delivery workflow
What's inside
12 sections covering purpose, roles, workflow steps, exceptions, automation, incidents, and metrics
Change this for your project
- Replace
Slack \#transcription-opswith your own notification channel - Replace
docs/prompts/with your own prompt template directory - Replace
videos/{yyyy}/{mm}/{request-id}/source.extwith your own naming convention - Replace
data/transcripts/{request-id}.jsonanddata/summaries/{request-id}/with your own storage paths
Where it goes
A standard operating procedure. Keep where the team or agent running the process will find it.
Worth borrowing
- Separates transcription QA from summarization QA to catch errors at each stage
- Defines a clear escalation path for low-confidence transcripts and summarization drift
- Includes a metrics and continual improvement section to track turnaround time and accuracy
Related Documents
Comprehensive AI Assistant Tools Reference
Lists 80+ tools with MCP server associations, bulk support, parallel capability, resource impact, and execution type for AI agent workflows.
iOS Deployment Guide
Walks through setting up an iOS development environment, building a Tauri app for iOS, and publishing to the App Store or alternative channels.
How to Add Resources to Your FastMCP Server
Teaches how to add static and dynamic MCP resources to a FastMCP server, with six ready-to-copy examples for a GitHub crawler.
PCI Compliance Guide
Explains PCI DSS compliance via tokenization, lists forbidden and allowed data, and provides code examples for BigCommerce and Stripe.