Together Evaluations

Together AI Evaluations

Overview

Use Together AI evaluations when the user wants a managed LLM-as-a-judge workflow rather than an ad hoc prompt loop.

Core evaluation types:

• Classify: assign outputs to labels
• Score: grade outputs on a numeric scale
• Compare: compare two candidate outputs with bias controls

This skill also covers external providers used as judges or targets when the workflow still runs through Together AI's evaluation system.

When This Skill Wins

• Benchmark prompt variants, models, or product responses
• Grade quality, safety, policy compliance, or task success
• Run A/B comparisons between model outputs
• Build repeatable evaluation jobs with uploaded datasets
• Pull results programmatically after asynchronous execution

Hand Off To Another Skill

• Use together-chat-completions for one-off inference or manual judge prompts
• Use together-batch-inference for bulk offline generation rather than evaluation
• Use together-fine-tuning when the user wants to improve the model instead of just measure it
• Use together-dedicated-model-inference only if the evaluation target itself is a dedicated endpoint

Quick Routing

• Classify / Score / Compare job setup
  • Start with scripts/run_evaluation.py or scripts/run_evaluation.ts
  • Read references/api-reference.md for exact request shapes
• Dataset formatting
  • Read the dataset sections in references/api-reference.md
• Dataset columns, Jinja2 templates, or pre-generated responses
  • Read the dataset and template sections in references/api-reference.md
  • Use --eval-column, --model-a-column, or --model-b-column in the scripts
• Evaluating vision-capable models with image inputs
  • Read the Image Inputs section in references/api-reference.md
  • Add an image_data_urls column (base64 data URLs) to the dataset; the model and judge must be vision-capable
• External providers as judge or target
  • Read the model-source and provider sections in references/api-reference.md
  • Use the scripts with --judge-model-source external, --eval-model-source external, or compare-side source flags
• Polling, listing, or downloading results
  • Use the retrieval endpoints documented in references/api-reference.md
  • Use --download-results in the scripts when you want the per-row JSONL locally

Workflow

1. Identify whether the user needs classify, score, or compare.
2. Define the dataset schema before writing code.
3. Upload the dataset as an eval file and keep the returned file ID.
4. Configure judge and target models explicitly, especially when mixing providers.
5. Poll status until completion, then download the result file for analysis.

High-Signal Rules

• Python scripts require the Together v2 SDK (together>=2.0.0). If the user is on an older version, they must upgrade first: uv pip install --upgrade "together>=2.0.0".
• The current SDK examples in this repo use check=False for eval uploads because local file validation can misclassify eval datasets.
• Treat dataset schema as part of the product contract; inconsistent fields cause downstream confusion.
• Compare evaluations are best when both candidate responses are already present in the dataset.
• Keep judge configuration explicit. Hidden defaults make benchmark interpretation harder.
• Use Together AI's managed evaluation job instead of rebuilding a manual judge loop when repeatability matters.

Resource Map

• Full API reference: references/api-reference.md
• Dataset formats, Jinja2 templates, and provider shortcuts: references/api-reference.md
• Python workflow: scripts/run_evaluation.py
• TypeScript workflow: scripts/run_evaluation.ts

Official Docs

• AI Evaluations
• Evaluations API