How do you organize prompt evaluation datasets for multiple Azure AI Foundry projects?

Question

How do you organize prompt evaluation datasets for multiple Azure AI Foundry projects?

jamess jamess 85

I'm working with multiple prompt-based applications in Azure AI Foundry and want to establish a consistent evaluation workflow.

When managing several projects, do you maintain a separate JSONL evaluation dataset for each application, or do you keep a shared dataset with different subsets for specific scenarios?

I'm also interested in how you organize versioning when prompts evolve over time. Do you create new evaluation datasets for major prompt changes, or do you keep the same benchmark dataset so results remain comparable across versions?

I'd appreciate hearing how others structure their evaluation process and any best practices for maintaining reliable benchmark data over the long term.

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2 answers

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Answer 1

Hi @jamess jamess

Scaling prompt evaluation across multiple Azure AI Foundry projects requires separating application-specific test cases while anchoring prompt iterations against immutable golden benchmark datasets.

Quick Fixes / Solutions:

Hub-Level Shared Assets: Register core JSONL test datasets at the Azure AI Foundry Hub level so child projects can share baseline benchmarks without duplicate uploads.
Immutable Golden Benchmarks: Keep a frozen "Golden Benchmark" (v1.0) to measure regression across prompt changes, creating new dataset versions only when schemas or business requirements change.
Automate with Evaluation SDK: Run the azure-ai-evaluation SDK in CI/CD pipelines to log comparative scores (Groundedness, Relevance, Coherence) automatically against your benchmark.

Ref: Evaluate generative AI apps with Azure AI Evaluation SDK

Answer 2

Hello jamess jamess,

A practical approach is to treat evaluation datasets as long-term benchmark assets instead of creating new ones for every prompt update.

If you're working with multiple Azure AI Foundry projects, you can maintain a shared benchmark dataset for common scenarios, such as summarization, Q&A, or classification, and use separate datasets for application-specific cases. This helps you compare results consistently across projects while still covering scenarios unique to each application.

When prompts change, it's generally helpful to keep the benchmark dataset stable so results remain comparable over time. As you discover new edge cases or production issues, add them as new test cases. If the evaluation goals or expected behavior change significantly, consider creating a new version of the dataset while retaining the previous version for historical comparisons.

A simple workflow could be:

Maintain a shared benchmark dataset for common scenarios.
Use separate datasets for project-specific testing.
Evaluate each prompt version against the same benchmark to identify improvements or regressions.
Add new regression test cases based on real-world issues.
Create a new dataset version only when the evaluation objectives change.

Azure AI Foundry supports uploading versioned JSONL or CSV datasets that can be reused across multiple evaluation runs. While the documentation explains how to create, version, and use evaluation datasets, the way you organize and version them is left to your team's workflow.

Reference:

https://learn.microsoft.com/azure/ai-foundry/how-to/develop/cloud-evaluation

jamess jamess 85 Reputation points

2026-08-05T11:24:24.1166667+00:00

Thank you for the detailed explanation and for sharing the documentation. Your recommendations on maintaining a stable benchmark dataset and adding regression cases over time were very helpful. I appreciate your guidance.