AI case
Prompt governance and evolution for Artificial Intelligence products
An internal tool that turns prompts into traceable, comparable, governed assets—bringing experimentation, review, and publishing closer together.
Context
A prompt becomes software when it supports a product.
Prompts needed to evolve with control, traceability, and an experimentation experience less dependent on external tools. The challenge was not only writing better instructions, but creating a place to observe, compare, and maintain what worked.
- Version changes and decisions
- Compare responses across models
- Shorten the distance between testing and use
Product question
How do you give every change context without slowing the experiment down?
The direction was to treat the prompt as an asset that can be edited, run, evaluated, and reviewed in the same flow. The product needed to support experiment speed without giving up an evolution trail.
- What changed?
- In what context was it tested?
- What evidence supports the next version?
Architecture decision
One workspace for editing, playground, and versioning.
The solution combines a parameterized editor, multi-LLM playground, commit history, and Git integration. The conceptual architecture organizes the work around context, not around the model provider.
- Editor and variables
- Multi-LLM runs
- Diff, history, and publishing
Evaluation
Comparison needs to be recoverable to become a decision.
The evaluation flow starts with comparable scenarios and inputs. The public interface does not present metrics as final results: it shows the place where criteria, responses, and decisions can come together.
- Versioned test cases
- Quality and reliability signals
- A record of uncertainty
Conceptual architecture
One workspace for editing, playground, and versioning.
The conceptual architecture organizes the work around context, not around the model provider.
Workspace
Context, variables, and solution intent.
Editor
Readable, parameterized, versionable prompts.
Models
Comparable runs across providers.
Evals
Criteria for learning from each round.
Git
History and collaboration for evolution.
Featured learning