prompt-tunning

Bias-free prompt tuning is an iterative process that executes prompts and collects dual evaluations to improve agent behavior. It targets skill, slash command, and task prompts by applying self-reports and external metrics across multiple scenarios to drive measurable, auditable improvements.

To start prompt tuning, initiate a tuning session for your target skill by running a baseline prompt. Then, begin the first iteration to collect dual evaluations, utilizing subagents for self-reports and directive metrics to enforce structured improvements.

Yes, you can use this prompt tuning process for AI agents in development, QA, and deployment. It stabilizes command handling and reduces ambiguity by removing biases and producing auditable improvements across multiple prompts.

The best way to evaluate prompt improvements is by applying iterative cycles with dual evaluations. This approach combines self-reports from subagents with external directive metrics, enforcing structured reporting and a safe, auditable workflow across various scenarios.

Yes, prompt tuning supports slash commands and task prompts used by autonomous agents. It automates the execution and evaluation of these prompts to iteratively remove biases and drive reliable behavior improvements.

Inconsistent agent outputs often stem from prompt biases and ambiguity. Applying an iterative prompt tuning cycle with dual evaluations and directive metrics removes these biases, enforcing a structured workflow to stabilize behavior across multiple scenarios.