When it comes to deploying your AI application in a Salesforce org, meticulous planning and structured steps are essential to ensure a seamless transition from development to production.
This guide outlines the process for going live with GPTfy, from sandbox testing to full production deployment, with a specific focus on the trial-and-error nature of the generative AI and the latest improvements in GPTfy’s functionality.
Step 1: Test Prompts in Sandbox with Realistic Data
Start by validating your AI prompts against production-like data:
- Use records that closely mirror real-world scenarios to evaluate prompt accuracy and quality.
- Include edge cases, data variations, and discrepancies in field completeness.
- Focus on the AI’s performance, relevance, and prevention of hallucinations.
Testing in this phase helps identify any prompt flaws early, ensuring they’re fixed before they reach your end users.
Step 2: Export Prompts, Data Context Mapping, and AI Models
Once validated, use GPTfy’s built-in import/export utility to package:
- Unit-tested prompts
- Linked data context mappings
- AI model configurations
Export everything as a JSON file for easy import into higher environments like UAT or Production.
Note - This ensures zero rework or manual configuration errors.
- Checkbox Placement: A checkbox is now placed next to the prompt name in the export list view, replacing the previous cross-out (X) icon. This provides a more user-friendly experience for selecting and deselecting prompts.
- Description Field Update: The description field is now wrapped, eliminating the need for horizontal scrolling. This change improves the readability and usability of the prompt list view.
- No Need of Deselecting Prompts: When a user deselects a prompt, they need to be cautious of the checkbox placement, as the cross-out (X) icon is no longer used. This change aims to reduce accidental selections and improve the workflow.
Step 3: Manually Transfer Any Related Apex Classes
If you’ve written custom Apex to:
- Call external APIs
- Handle authentication for AI Models
- Perform enrichment or transformation tasks
Be sure to manually copy or deploy these Apex classes to the next environment first. These are rare but critical if present.
Step 4: Import to UAT or Production
Now import your prompt package into the target org.
- Use GPTfy’s import tool to bring in Prompts, Context Mappings, and AI Models
- Validate the integrity of objects, fields, and model settings
- Ensure prompts reference the correct endpoints and credentials
Note- GPTfy will validate field references when you activate the prompt—fix any invalid fields before proceeding.
Step 5: Validate AI Model Connectivity
Confirm that AI models are responsive in the new org.
- Test Named Credentials or external auth mechanisms
- Activate a simple test prompt
- Review security audit logs for any failed API calls
You can also run a REST call via Apex in Developer Console to double-check connectivity.
Step 6: Add GPTfy Lightning Component to Page Layouts
Embed the GPTfy Console where users will access it.
- Update the Record Page Layout(s) for relevant objects
- Place the GPTfy Lightning Web Component on the desired tab or section
- For multi-layout orgs, map components based on Profile, Record Type, or Page Assignment rules
Step 7: Assign and Test Prompts One at a Time
Take a controlled rollout approach:
- Assign the GPTfy permission set to a test user
- Activate and assign one prompt to their profile
- Test it on real records and confirm the output
Once verified, expand testing to more users in the same profile group.
Step 8: Verify Functionality as a New User
Use “Login As” to impersonate users and test:
- Prompt visibility
- Field-level access issues
- Unexpected behaviors due to Record Type, Profile, or custom automation
Note- If a prompt writes back to Salesforce, triple-check the test scenarios to avoid unintended updates.
Step 9: Gradually Enable for Pilot Group
Avoid enabling all prompts at once. Instead:
- Select a group of early adopters
- Monitor usage, feedback, and performance
- Scale up afterthe prompt behavior is consistent
This phased rollout improves user confidence and reduces disruption.
Step 10: Monitor and Audit Prompt Performance
Once in production:
- Monitor GPTfy audit logs and security events
- Use the in-product feedback option to collect user insights on prompt accuracy or bias
- Track usage volume and any failed responses
This feedback loop helps prompt engineers to refine performance over time.
Step 11: Apply Prompt Versioning and Change Control
For any prompt updates:
- Clone and modify the prompt in Sandbox
- Retest thoroughly
- Use GPTfy’s migration utility to move the updated version to Prod
- Keep version notes for traceability
Always treat prompts like production code—with testing, approvals, and rollback strategies.
Step 12: Support Change Management with Empathy
Remember, this may be your team’s first experience with AI in Salesforce.
- Provide user guides or short videos for using prompts
- Offer hands-on help during the rollout
- Be patient with questions—this helps build long-term trust in your AI rollout
Summary
A successful AI go-live with GPTfy depends on rigorous testing, structured migration, and thoughtful rollout. By following this checklist, you can confidently deploy prompts that are reliable, useful, and embraced by your end users.