Anthropic recently brought major updates to its console (Console), launching prompt word optimizer and sample management functions, aiming to help developers optimize prompt words and improve the reliability of AI applications. This update solves the problem of large differences in best practices of prompt words on different model platforms and time-consuming and labor-intensive optimization process. It significantly improves the quality of prompt words through automation, thereby improving the output effect of the AI model. The update includes five major optimization methods and supports user feedback and continuous improvement.
The quality of prompt words directly affects the output effect of the AI model. However, the best practices of prompt words for different model platforms vary, and the optimization process is often time-consuming and labor-intensive. In response to this pain point, Anthropic's prompt word optimizer can automatically use advanced engineering techniques to improve existing prompt words, which is particularly suitable for optimizing prompt words or handwritten prompt words written for other AI models.
Specifically, the optimizer uses five major methods to enhance the effect of prompt words: first, it introduces chain thinking reasoning, so that Claude can think about problems systematically before responding; second, it is to convert the examples into XML format to improve clarity; third, it is to convert the examples uniformly into XML format to improve clarity; It enriches existing examples with chain thinking that conforms to the new structure; the fourth is to rewrite the prompt words to optimize the structure and correct the grammatical spelling; and finally to pre-filling the Assistant information to guide Claude's behavior and output format.
Test data shows that this optimization system has increased the accuracy rate by 30% in multi-label classification tests and achieved 100% word accuracy in text summary tasks. Users can also provide feedback on the optimization results to further improve the effect of prompt words.
In terms of example management, developers can now manage examples directly in a structured format in the workbench. The system supports adding clear input/output pairing examples and editing existing examples to improve response quality. For prompt words without examples, Claude can also automatically generate synthesized sample input and output drafts, simplifying the entire process.
The well-known technology company Kapa.ai has successfully migrated multiple key AI workflows to the Claude platform with the help of this optimizer. "Anthropic's prompt word optimizer streamlined our migration to Claude3.5Sonnet, helping us get into production faster," said Finn Bauer, co-founder of the company.
Currently, the prompt word optimizer, sample management, and ideal output features are available to all Anthropic Console users. This system not only improves accuracy, but also ensures consistency in the output format, significantly enhancing Claude's ability to handle complex tasks. Developers can learn more about how to use Claude to improve and evaluate prompt words through the official Anthropic documentation.
Reference: https://www.anthropic.com/news/prompt-improver
In short, Anthropic update provides developers with powerful tools, significantly improving the efficiency of Claude's prompt word writing and management, thereby improving the accuracy and reliability of AI applications and accelerating the development and deployment of AI applications. Looking forward to Anthropic bringing more similar improvements in the future.