Together AI Evaluations Skill for Claude Code

What it is

The Together AI Evaluations Skill is a Claude Code skill that teaches the agent how to use Together AI's LLM evaluation framework. It enables you to run LLM-as-a-judge evaluations directly from Claude Code, scoring model outputs on quality, safety, and task completion. The skill encapsulates Together AI's evaluation API patterns so Claude Code can set up, run, and interpret evaluation results without manual API calls.

AI engineers and developers who need to evaluate LLM outputs systematically benefit most. Instead of manually reading outputs and scoring them, this skill automates the process using a judge model.

How it saves time or tokens

Manual evaluation of LLM outputs is slow and inconsistent. This skill automates the scoring process by routing outputs through Together AI's evaluation endpoints. Claude Code handles the setup, prompt construction for the judge model, result collection, and summary generation. A batch of 100 outputs that would take hours to review manually completes in minutes. The skill also standardizes evaluation criteria, reducing the variance that comes from human judgment.

How to use

1. Install the skill in your Claude Code configuration by adding the Together AI evaluations skill file to your project.

1. Set your Together AI API key:

export TOGETHER_API_KEY='your-api-key'

1. Ask Claude Code to evaluate outputs:

Evaluate these model outputs for answer quality using Together AI's judge framework

Claude Code will construct the evaluation prompts, call Together AI's API, and return structured scores.

Example

# Example evaluation configuration the skill uses
evaluation_config = {
    'judge_model': 'meta-llama/Llama-3-70b-chat-hf',
    'criteria': ['relevance', 'accuracy', 'completeness'],
    'scale': [1, 5],
    'dataset': 'eval_samples.jsonl'
}

# The skill generates judge prompts like:
judge_prompt = '''
Rate the following response on a scale of 1-5 for relevance.
Question: {question}
Response: {response}
Score (1-5):
'''

Related on TokRepo

• AI Monitoring Tools -- Evaluation and observability tools for AI
• Prompt Library -- Curated prompts including evaluation templates

Common pitfalls

• LLM-as-judge evaluations are only as good as the judge model and criteria. Poorly defined criteria produce inconsistent scores. Be specific about what 'quality' means for your use case.
• Together AI API calls incur costs. Evaluating large datasets with a 70B parameter judge model can become expensive. Start with a small sample to calibrate before running full evaluations.
• Judge model bias exists. Different judge models score differently. Validate your judge setup against a small set of human-labeled examples before trusting automated scores at scale.