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Weavel
Overview :
Weavel is an AI prompt engineer that assists users in optimizing applications of large language models (LLMs) through features like tracking, dataset management, batch testing, and evaluation. Combined with the Weavel SDK, it seamlessly integrates by automatically recording and adding LLM-generated data to your datasets, ensuring continuous improvement for specific use cases. Additionally, Weavel can automatically generate evaluation code, utilizing LLMs as unbiased judges for complex tasks, simplifying the evaluation process and ensuring accurate and detailed performance metrics.
Target Users :
The target audience is developers and enterprises seeking to enhance the performance of large language model applications. Weavel provides advanced prompt engineering tools to help them optimize models, improving the accuracy and efficiency of applications, particularly for users handling complex natural language processing tasks.
Use Cases
Businesses use Weavel to optimize responses for customer service chatbots.
Developers leverage the Weavel SDK to automatically log user interaction data for model training.
Educational institutions employ Weavel to assess the performance of teaching assistant robots.
Features
Tracking: Record and analyze LLM-generated data to optimize model performance.
Dataset Management: Automatically log and add data through the Weavel SDK without needing pre-existing datasets.
Batch Testing: Conduct large-scale tests to evaluate and compare the impacts of different prompts.
Evaluation: Automatically generate evaluation code and use LLMs as evaluation tools to ensure fairness and accuracy.
Continuous Optimization: Continuously refine prompts using real-world data.
CI/CD Integration: Prevent performance regression through continuous integration and deployment.
Human-in-the-loop: Implement human guidance and feedback through scoring and reviews.
How to Use
Visit the Weavel website and create an account.
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Configure the Weavel SDK and integrate it into your application.
Manage datasets and conduct batch testing using Weavel.
Set evaluation criteria to allow Weavel to automatically generate evaluation code.
Adjust prompts based on evaluation results to optimize LLM applications.
Utilize CI/CD integration to ensure continuous performance improvement.
Provide manual feedback to help Weavel learn and enhance its capabilities.
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