Back up AI prompts, LLM artifacts, and workflows
Preserve prompt versions, model references, retrieval inputs, and evaluation cases without confusing credentials with service credits.
Read the 6-minute guideAI workflows
Insta Backups AI Cloud
Connect the parts of your AI recovery plan.
Cloud-based AI projects often span hosted models, application code, prompt libraries, retrieval data, and object storage. Map those dependencies as one workflow while giving each artifact a suitable capture method. The goal is a release that another authorized environment can reconstruct and evaluate, not a promise that every hosted dependency can be exported.
What to preserve
- A release manifest connecting prompts, models, data, and tool versions.
- Permitted source documents and object-storage recovery references.
- Hosted-service access procedures and a clean-environment test plan.
Your starting workflow
Select one workflow and assign an owner to each dependency. Store recoverable artifacts independently where required, and record service identifiers for the parts you cannot copy. Preserve an evaluation set so a replacement dependency can be assessed rather than silently treated as identical.
Test the recovered copy
Rebuild the selected release without the original development environment. Retrieve its artifacts, supply authorized credentials, and run controlled evaluations with sandboxed tools. Document changes in models, data, or contracts and decide whether the result meets the written criteria.
What connects cloud storage to an AI workflow backup?
The manifest identifies which stored artifacts belong to the release and how they are loaded. Pair storage-level checks with a task-level evaluation so intact objects are not mistaken for a working application.
From Insta Backups Lab
Put the plan into practice.