Anthropic recently brought a major update to its console (Console), launching a prompt word optimizer and sample management functions, aiming to help developers build reliable AI applications more easily. The editors of Downcodes will take you to take an in-depth look at this update to see how it simplifies prompt engineering and improves the performance of AI models.
Anthropic recently launched an important update in its Console, bringing developers new features in prompt word optimization and sample management. This upgrade will help developers more easily apply prompt engineering best practices and create more reliable AI applications.
The quality of the prompt words directly affects the output effect of the AI model. However, best practices for prompt words vary across model platforms, and the optimization process is often time-consuming and laborious. To address this pain point, the prompt word optimizer launched by Anthropic can automatically use advanced engineering technology to improve existing prompt words. It is especially suitable for optimizing prompt words or handwritten prompt words written for other AI models.
Specifically, the optimizer uses five methods to enhance the effect of prompt words: first, it introduces chain thinking reasoning to let Claude think about the problem systematically before responding; second, it uniformly converts examples into XML format to improve clarity; third, The fourth step is to enrich existing examples with chain thinking that conforms to the new structure; the fourth step is to rewrite the prompt words to optimize the structure and correct the grammar and spelling; the last step is to pre-fill Assistant information to guide Claude's behavior and output format.
Test data shows that this optimized system increased the accuracy by 30% in the multi-label classification test and achieved 100% word count accuracy in the text summary task. Users can also provide feedback on the optimization results to further improve the prompt word effect.
In terms of example management, developers can now manage examples in a structured format directly 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 synthetic example input and output drafts, simplifying the entire process.
Kapa.ai, a well-known technology company, has successfully migrated multiple key AI workflows to the Claude platform with the help of this optimizer. Finn Bauer, co-founder of the company, said: Anthropic’s prompt word optimizer streamlined our migration process to Claude3.5Sonnet and helped us get to production faster.
Currently, the prompt word optimizer, sample management and ideal output functions are open to all Anthropic Console users. This system not only improves accuracy, but also ensures consistency in output formats, significantly enhancing Claude's ability to handle complex tasks. Developers can learn more details about how to use Claude to improve and evaluate prompt words through the official Anthropic documentation.
Reference: https://www.anthropic.com/news/prompt-imrover
All in all, Anthropic’s update provides developers with powerful tools that significantly improve Claude’s efficiency and reliability. It is believed that this update will promote the further development of AI applications. The editor of Downcodes recommends that you visit the official Anthropic documentation for more details.