human-eval for ML Modeling

Why human-eval for for ml modeling

human-eval is data scientists, ML engineers, and analysts. For running ML experiments at scale, the typical workflow is:

1. Define the input. Gather the data, context, or prompt you'll feed in.
2. Set up the template. Build a reusable prompt in human-eval that handles your common case.
3. Run on a small batch. Test on 5-10 examples. Check quality before scaling.
4. Iterate on the prompt. Most teams spend 30-90 min refining the prompt before they get consistent results.
5. Wire into the workflow. Either via human-eval's built-in features, or an API/script.

What you can do with human-eval for ml modeling

• Analyzing datasets. human-eval is well-suited for analyzing datasets in this context. Most teams see 2-5x speedup vs. manual.
• Training models. human-eval is well-suited for training models in this context. Most teams see 2-5x speedup vs. manual.
• Fine-tuning LLMs. human-eval is well-suited for fine-tuning LLMs in this context. Most teams see 2-5x speedup vs. manual.
• Dashboards. human-eval is well-suited for dashboards in this context. Most teams see 2-5x speedup vs. manual.

Real example prompts

For solo work:

Help me train and evaluate ML models for the next 30 minutes. I have these inputs: [paste]. Output: a clear, ready-to-use draft.

For team use:

I'm on a small team. We need to train and evaluate ML models. Suggest a workflow, the prompts we'd need, and how to measure success.

For client work:

Generate 3 different versions of [output] for client X. Each should be on-brand and ready to send after light editing.