Cohere Prompt Tuner : Smartly optimizes prompts to enhance the performance of generative models

Cohere Prompt Tuner

Development & Tools AI Model #AI #Optimization #Automation #Prompt Engineering Editor's Picks Paid

Overview :

Cohere Prompt Tuner is a tool for optimizing prompts for generative models. It improves prompts through a customized optimization and evaluation cycle, thereby enhancing the effectiveness of model outputs. This tool automates the manual aspects of prompt engineering and unlocks capabilities that traditional prompt engineering finds challenging to achieve. In internal assessments, the Prompt Tuner successfully optimized 94% of use cases, significantly improving the efficiency of the development process.

Target Users :

Cohere Prompt Tuner is designed for enterprise AI engineers who need to optimize the output of generative models. It is particularly suitable for scenarios that require extensive testing and evaluation to fine-tune prompts, helping engineers save time, improve efficiency, and ensure the accuracy and impartiality of the prompts.

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Use Cases

In the conversion from natural language to SQL, the prompt score improved from 39% to 100%.

In entity extraction, the prompt score increased from 73% to 93%.

The optimized prompts enhanced human readability and improved machine accuracy through ISO format requirements.

Features

Automatically optimize prompts to enhance the output of generative language models

Customize evaluation criteria to guide instruction generation and create new prompts

Automate prompt engineering to reduce manual testing and evaluation cycles

Support the transition to new models and the expansion of new use cases, improving the efficiency of prompt engineering

Continuously improve prompts through iterative optimization until perfect scores are achieved

Internal testing shows an average improvement of 11.5%, significantly enhancing specific use cases

How to Use

Set the initial prompt: Create the initial prompt that needs optimization, including any variables or documents.

Define success criteria: Establish clear success criteria for the prompt, selecting multiple metrics including accuracy, character count limits, etc.

Obtain optimized prompts: Observe the model as it generates optimized prompts based on the success criteria.

Generate completions: The target model generates completions for each fully presented prompt based on the initial prompt.

Evaluate completions: The Prompt Tuner submits the generated completions to the evaluation service to obtain standardized scores based on the selected criteria.

Participate in instruction generation: Use Command R+ as the instruction generation model, processing data to generate improved prompts that are expected to perform better according to the evaluation criteria.

Iterate: The Prompt Tuner extracts the revised prompt templates, reinserts variables, and uses the target model to generate new completions. This cycle repeats continuously, optimizing prompts progressively.

End optimization cycle: The optimization process continues until the prompt achieves a perfect score or reaches the allowed maximum number of iterations. At this point, the system finalizes and provides the best-performing prompt to the user.