Together Evaluations

What it does

The together-evaluations skill allows AI agents to evaluate outputs from other models, providing feedback and scoring based on defined criteria. It can assess responses for relevance, accuracy, coherence, and overall quality. This enables iterative improvement of model performance through targeted analysis and refinement.

When to use it

• Model Comparison: Evaluate the output of multiple language models against a specific task or prompt to determine which performs best.
• Feedback Loop: Integrate evaluation scores into a training pipeline to automatically refine and improve a model's responses over time.
• Content Quality Control: Assess generated content (e.g., articles, code) for quality before publication or deployment.
• Prompt Engineering Optimization: Analyze how different prompts impact the quality of model outputs and identify optimal prompt formulations.

Key capabilities

• Model output evaluation
• Scoring based on defined criteria
• Feedback generation
• Relevance assessment
• Accuracy verification

Example prompts

• "Evaluate this response: [model output] against these criteria: relevance, accuracy, and coherence."
• "Score the following text for quality, considering grammar, clarity, and factual correctness: [text to evaluate]."
• "Compare the outputs of Model A and Model B given the prompt 'Summarize this article:' [article content]. Provide a detailed evaluation of each response."

Tips & gotchas

The effectiveness of this skill depends heavily on clearly defined evaluation criteria. Ambiguous or poorly-defined criteria will lead to inconsistent results.