Building AI products is a team sport. Engineers write the application logic. Prompt engineers craft the instructions. Product managers define the requirements. Domain experts validate the outputs. Yet most prompt workflows only serve one of these roles — typically the developer.
The result? Everyone else is blocked. The prompt engineer sends their edits via Slack. The PM creates a Jira ticket for a one-word change. The domain expert reviews outputs from a screenshot, not the actual prompt.
A good prompt workflow serves the entire team.
The Cross-Functional Challenge
Each role has different needs when it comes to prompts:
| Role | Needs | Blocked By |
|------|-------|------------|
| Engineer | Clean API, no prompt logic in code | Prompt changes requiring deploys |
| Prompt Engineer | Direct editing, version history, quick iteration | Needing code access to change prompts |
| Product Manager | Visibility into changes, ability to test variants | No dashboard or testing environment |
| Domain Expert | Ability to review and refine content | Technical barriers to accessing prompts |
A workflow that requires Git access for every prompt change excludes three out of four roles.
Designing an Inclusive Workflow
Step 1: Centralize Access
Give everyone a dashboard where they can view, edit, and manage prompts. This doesn't mean everyone has the same permissions — it means everyone has access to the tools they need for their role.
- Engineers set up the initial prompts and API integration
- Prompt engineers edit content and manage versions
- PMs review changes and monitor performance
- Domain experts review prompt content for accuracy
Step 2: Separate Concerns
The key insight is that prompt content and application code should evolve independently:
- Application code defines where prompts are used, how they're fetched, and how responses are processed. This changes infrequently and requires engineering review.
- Prompt content defines what the AI model does. This changes frequently and benefits from input by the entire team.
By separating these concerns, prompt content changes don't require code deploys, and code changes don't require prompt expertise.
Step 3: Use Staging for Validation
A staging environment lets team members test prompt changes before they affect real users:
- Prompt engineer edits the prompt in staging
- Domain expert reviews the new content for accuracy
- PM tests representative scenarios in the staging app
- Engineer verifies the API integration still works
- Promote to production when everyone is satisfied
This mirrors how product teams already work — the difference is that prompt changes no longer need to go through the code deployment pipeline.
Step 4: Version Everything
When multiple people contribute to prompts, version history becomes essential:
- See who changed what and when
- Understand why a change was made
- Roll back if a change causes issues
- Compare versions to understand the evolution of a prompt
Versioning creates accountability and transparency across the team.
Step 5: Establish Review Practices
Not every prompt change needs formal review, but high-impact prompts should have a review process:
- Low-impact changes (fixing typos, minor rephrasing): Edit and promote
- Medium-impact changes (adding new instructions, changing tone): Peer review in staging
- High-impact changes (new prompts, major restructuring): Team review with testing
The review rigor should match the blast radius of the change.
Common Workflow Patterns
The Prompt Sprint
Dedicate time for the team to collectively improve prompts:
- PM identifies prompts with low user satisfaction
- Team reviews current prompt content and recent outputs
- Prompt engineer proposes changes
- Domain expert validates accuracy
- Changes are tested in staging and promoted
The Feedback Loop
Create a continuous improvement cycle:
- Users provide feedback on AI outputs
- PM categorizes feedback and identifies prompt-related issues
- Prompt engineer adjusts prompts based on feedback
- Changes are tested and promoted
- Monitor whether user satisfaction improves
The New Feature Workflow
When launching a new AI feature:
- PM defines requirements and expected behavior
- Engineer builds the integration and creates the initial prompt
- Prompt engineer refines the prompt for quality
- Domain expert reviews for accuracy and compliance
- Team tests in staging
- Promote to production at launch
Anti-Patterns to Avoid
The Developer Bottleneck: All prompt changes go through one developer. This slows everyone down and doesn't scale.
The Slack-Driven Change: Someone pastes a prompt in Slack and asks a developer to "put it in." No version history, no testing, no accountability.
The Copy-Paste Deploy: Someone copies a prompt from a doc into the codebase. Formatting issues, missing variables, and no validation.
The "Just Ship It" Approach: Prompt changes go directly to production without staging. Works until it doesn't.
Enabling the Workflow with FetchPrompt
FetchPrompt gives teams the infrastructure for collaborative prompt management:
- Dashboard access for everyone — no Git or code knowledge required
- Version history tracks every change with who, when, and what
- Staging and production environments enable safe testing before promotion
- REST API keeps application code decoupled from prompt content
- Variable interpolation makes prompts flexible without code changes
The goal is to create a workflow where the right people can make changes at the right time, with the right safety nets in place.