Amazon Bedrock Prompt Popt Optimiooning Drives in Innovation Applications for Yuewen Group
Yuewen Group is a global leader in online literature and IP operation. With its WebNovel overseas, dragging some 260 million users in over 200 and regional countries, to promote China's population worldwide. The company adapts the high-quality web novels of films, international markets, increases the world's influence of the Chinese culture.
Today, we are happy to announce the availability of amazon bedrock. By power, you can now increase your number of cases of one apex or by clicking the Amazon Bedrock Console. In this blog course, we discuss how efficiency is to improve the operation of large languages (lls) for the wise text process in Guewen Group.
Evolution from NLP is traditional in LLM in the performance of the smart text
Yuewen Group Leverages AI of a wise analysis of the broadcast vocabulary. At first you rely on natural language (NLP) models, the Yuewen Group faced challenges on cycles for several development cycles and a slight renewal. Improving efficiency and efficiency, Yuewen Group has been converted to Anththropic's Claude 3.5 Sonnet in Amazon Bedrock.
Claude 3.5 Sonnet gives the development of developed natural language and generation skills, managing many functions and advanced understanding of the context and normal. Using Amazon Bedrock is severely reduced Heathy Highhead technology and the process of improving improvement.
However, the Yuewen party was originally fought hard to fully integrate the LLM opportunities because of limited experiences in the immediate engineer. In some cases, the performance of the llm was only the traditional NLP models. For example, in the “Charac Dialogue Attribution” work, traditional NLP models are available with 80% accurate, and Ills have any detained arguments reached approximately 70%. This diversity has highlighted the need for efficiency of the skills development strategies for llMs for specific charges.
Challenges early in the morning
Fast speed efficiency can be challenging for the following reasons:
Difficulty in the test: To assess the speedy quality and harmony in displaying desirable responses from the language model. Effective performance can be determined only in quick quality, but also by its communication with a specific language model, depending on its construction and training data. This Interplay requires a great domain technology to understand and wander. In addition, evaluating the quality of open work is usually involved in the default and effective judgment, which makes it challenging to establish methods to use purpose and bundle.
Dependence on condition: The fastest effective performance is very prone to certain situations and apply cases. Prompt that works well in one situation may attract another, requires comprehensive custom planning and planning different apps. Therefore, developing a well-efficient way of working properly in various activities is always a major challenge.
Scale: Since the llms receives apps by growing number of cases of use, the number of required instructions and the difficulties of the language model continue to increase. This makes the manufacture of the booklet in accordance with food and hard work. Composing and promoting the cutting of major applications may not work quickly and not working properly. In the meantime, as rapid variation rises, the full lift site increases, providing written tests of all invisible computers, even complex lifts.
Given these challenges, the default technological technology has taken important attention to AI. In particular, the performance of the Bedrock Prompt Optimization provides two advanced benefits:
- Working well: It saves a long time and effort by producing high quality quality is ready for various llms-based llms, disclose the need for special model engineering.
- Development of Operation: Ai-optimalization of AI by creating prepared preparations for the quality improvement of the Language Models in all functions and tools.
These benefits are not limited to the development process, but also lead to effective and effective AI requests, default as promising development in the wild.
Introduction to the fastest Drock and do well
Fast functionality in Amazon Bedrock is a feature of AI-Contacts We intend to automatically add improved customer service, improving the performance of the llMS and different functions. The efficiency of the Tress is integrated outside the seams of the Amazon Bedrock Playground and speed management to create easily, testing, storing and fast-effective use of your AI programs.
In the AWS Management Console for instant management, users put their way forward. Prespos can be a moderate format that is represented by the local editors (eg after selecting a single target, and the prepared and prepared is the fastest composite. Defense processing and production format you want. Users can see prompts made with Prompt Optimization to improve fast performance for their specific work.
Complete assessment is made in open data for some functions including division, summarizing, Open-Book Queing Customer, Displays the Prepared Payment.
Making low-income process, speedy analytics and quick author is integrated with the speedy expanding. PROMPT Analyzer is a good llm organization that impresses immediate composition by issuing its important forms, such as employment education, installation, and fewer shooting. Emergency elements are distributed to the rewrite text module, using the Meta lift strategy based on the promotion signing and redesigning prompts. As a result, the instant writer produces a refined and improved type of development for the llim target.
Effects of Well Use
Using a Bedrock Prompress Optimization, the Yuewen Group is reached for a major development in all various tasks of analyzing, including the issuing of the name and variety of use – several use charges. Take a role of weather discussion as an example, well-made products have reached 90% accuracy, exceeds traditional NLP models at 10% of each customer test.
Using the power of Foundation Models, the fastest efficiency produces higher Minimum Manual Templess Stract Itemation. Most importantly, this feature has enabled Yuewen Group to complete the speedy engineering processes in the time, even better improving efficiency.
Prompt Optimization Exploalization Best
Through all our knowledge of working properly, we have included a number of advice on better user experience:
- Use clear removal and accuracy of installation: Fast functionality will benefit from clear purposes and key expectations in your installation revival. Also, a clear strain structure can give a better start to do well immediately. For example, to distinguish quick stages with new lines.
- Use English as Tongue of Installation: We recommend using English as a quick space tongue. Currently, encouraging containing a large rate of other languages may not manufacture the best results.
- Avoid quick fast and examples: Excessive tip and fewer shots and examples shot mostly to grow semantic understanding and challenge redesign limit. Another tip to avoid fans in the same sentence and remove the real context about the bottom of the floor of the floor, for example {section} “Category: } nathor: n { {{{ {{{{{{{{{{{{{{{{{{{query}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}} {
- Work In the first stage of quick engineering: PRESSOPTIATION EXCLOBELSE FOR EXCLUSIVE EXCLUSIVE EFFECTION (not “lazy improvements may be very important in such encouraging when compared to those who are carefully.
Store
Amazon Bedrock's efficiency has been proven that it is a change of Yuewen Group game in their discreet text. By improving the accuracy of jobs such as the form of a variety of empties and is submitting a quick engineering process, the acceleration process has enabled Yuewen Group to completely integrate the strength of the llms. The trial of the case demonstrates the power of effective operations that change the LLM applications throughout the industry, providing saving time and performance development. Since AI continues to appear, the same tools will play an important role in helping businesses to increase the benefits of the LLM.
We encourage you to check the prompt improvement to improve the performance of your AI requests. To get started by doing well, see the following resources:
- Amazon Bedrock Pricing page
- Amazon Bedrock User Guide
- Amazon Bedrock API index
About the authors
Rui Wang Is the construction of senior solutions in AWs with extensive information in sport performance and development. As an enthusiastic AI lawyer, she enjoys examining Ai infrastructure and LLM Development Development. In his spare time, you like to eat a hot pot.
Hao huang Used scientist at the AWS Generative Ai Innovation Center. His technology is killed in AI, with a computer idea, and a faithful AI. Hao also gives a scientific community as a reviewer of the leading AI and magazines, including CVPR, AAAI, and TMM.
Guang YangPhi.d. Is the Senior using a scientist with the Generative Ai Innovation Center at Aw. He had 5 yrs's AWS hair, leading many customers' projects in the main level of Chinese Chinese as software, productivity, using more than 10+ solutions to build business problems.
Zhengen Shen He is a used scientist in Amazon Bedrock, who cares for the founders and ML model of the MLIs of complex tasks including the environment and understanding of formal data. You are interested in the new ML-ML solutions to the development of products or services, when you facilitate customer life using a mixture of science and engineering. Outside work, he enjoys sports and cooking.
Huong Nguyen It is the main product manager for AWS. He is a product leader in Amazon Bedrock, at the age of 18 for the experience of creating customer products and the data. You are interested in the democratic democracy and AI generous AI to enable customer experience and business establishment. Outside work, she enjoys spending time with family and friends, listening to Audiobooks, walking, and garden.
