Core Insights - The article discusses the upgrades and new features of Volcano Engine's AI tools, including PromptPilot and Data Agent, aimed at enhancing AI application efficiency and data utilization for enterprises [1][2][4]. Group 1: PromptPilot Upgrade - PromptPilot has been upgraded to support prompt optimization for any model, including public cloud models, private models, and custom-trained models [2][3]. - The tool utilizes natural language interaction to understand user needs, extract evaluation criteria, and generate improved prompts, thereby continuously optimizing based on online traffic and bad case analysis [2][3]. - The integration with Volcano Engine's knowledge base allows for precise content retrieval, enhancing the model's understanding and output in specialized fields [3]. Group 2: Data Agent and One-Customer-One-Strategy - Data Agent is a vertical intelligence tool that deeply understands and utilizes enterprise data assets, enabling proactive analysis and action [5]. - The "One-Customer-One-Strategy" feature allows for personalized marketing by analyzing multi-dimensional data related to customers, internal knowledge bases, and public data, generating tailored marketing plans [5][6]. - This feature has shown significant results, with conversion efficiency from Marketing Qualified Leads (MQL) to Sales Qualified Leads (SQL) increasing by up to 300%, and data utilization rising from 10% to 95% [6]. Group 3: AI Data Lake Service and AI Operator Square - The AI Data Lake service has been enhanced with the launch of "AI Operator Square," which facilitates the management of multi-modal data, including text, images, and audio-visual content [7]. - The AI Operator Square provides over 100 standardized operators for various data processing tasks, allowing users to create modular workflows through a visual drag-and-drop interface [8][9]. - This upgrade aims to transform scattered data into knowledge assets, promoting automated circulation and value addition of knowledge assets [9].

Core Insights - The article discusses the upgrades of Volcano Engine's PromptPilot and Data Agent, which enhance AI applications and data management for enterprises. Group 1: PromptPilot Upgrade - PromptPilot has been upgraded to support prompt optimization for any model, including public cloud models, private models, and custom-trained models [2][3] - The tool utilizes natural language interaction to understand user needs, extract evaluation criteria, and generate better prompts, thus improving AI application performance [2][3] - After deployment, PromptPilot can sample online traffic, analyze bad cases, and autonomously optimize prompts, creating a cycle of continuous improvement [2][3] Group 2: Data Agent and Multi-modal Data Lake - Data Agent is introduced as a vertical intelligent agent that deeply understands and utilizes enterprise data assets, enabling proactive analysis and action [4] - The "One Customer, One Strategy" capability of the intelligent marketing agent integrates three types of data for precise customer profiling and targeted marketing strategies [4][5] - The effectiveness of "One Customer, One Strategy" includes a 300% increase in conversion efficiency from MQL to SQL, a rise in data utilization from 10% to 95%, and a reduction in customer analysis time from 30 minutes to 2 minutes [5] Group 3: AI Operator Square - The AI Data Lake service has launched the "AI Operator Square," which integrates management of multi-modal data, including text, images, and audio-visual content [5][6] - The platform offers over 100 standardized operators and supports the integration of mainstream open-source operators, providing a comprehensive framework for custom operator development [6] - Users can visually drag and drop to quickly assemble modular workflows, transforming scattered data into knowledge assets for automated circulation and value addition [7]

Core Insights - The article discusses the evolution of prompt engineering in AI, emphasizing its importance in enhancing the interaction between users and AI models [4][16][65] - It highlights the differences in AI model performance based on the quality of prompts used, suggesting that effective prompt engineering can significantly improve AI outputs [3][16][65] Group 1: Evolution of Prompt Engineering - The evolution of prompts has progressed through three stages: "Magic Spell" era, "Enlightenment and Guidance" era, and "Systematic Engineering" era [10][11][14] - In the "Magic Spell" era, users treated AI like a search engine, leading to inconsistent results [10] - The "Enlightenment and Guidance" era introduced techniques like example learning and thinking chains, improving AI's reasoning and logic capabilities [12][13] - The current "Systematic Engineering" era requires structured prompts that include roles, objectives, constraints, examples, and steps to ensure stable and controllable AI outputs [14][15] Group 2: Importance of Prompt Engineering - Prompt engineering is defined as the science of designing and optimizing prompts to effectively communicate with large language models, directly impacting the quality of AI outputs [16] - High-quality prompts reduce the likelihood of AI generating "hallucinations" and help uncover the AI's potential for complex tasks [17] - The R.O.L.E.S. framework (Role, Objective, Limit & Constraint, Examples, Steps) is introduced as a method for creating effective prompts [17][18][20][22][26][28] Group 3: ByteDance's PromptPilot - ByteDance launched PromptPilot, a platform aimed at optimizing the entire process of AI model application, from concept to deployment and iteration [35] - The platform offers features for prompt generation and optimization, making it accessible for users without prior prompt writing experience [39] - Users can validate and refine prompts through various tuning modes, enhancing the effectiveness of AI-generated outputs [40][41][62] Group 4: Conclusion and Future Implications - The article concludes that mastering prompt engineering is essential for leveraging AI effectively, transforming it into a foundational skill for future interactions with AI [65][66] - While PromptPilot is not perfect, it serves as a valuable tool for users to develop structured thinking and improve their interactions with AI [67][70]

Core Insights - The article discusses the launch of PromptPilot, an intelligent solution platform designed for large models, which aims to transform vague user ideas into precise AI instructions, ensuring high-quality output from models [1][2]. Group 1: Product Features - PromptPilot automates the entire lifecycle of prompt generation, debugging, optimization, and iteration, freeing users from tedious tasks [3]. - The platform acts as a "demand translator," helping users clarify their needs through interactive guidance [3]. - It simplifies the process of defining ideal answers by allowing users to select from diverse generated responses, facilitating quick understanding of user intent [3][4]. - PromptPilot incorporates a closed-loop optimization system that turns "Bad Cases" into data assets for continuous improvement [3][4]. Group 2: Advanced Capabilities - The platform simulates human-like reflection and error summarization, enabling automatic iterative optimization to find the "golden question" for stable results [4]. - It supports multi-turn dialogue optimization, allowing for real-time feedback and enhancement in complex conversational scenarios [5]. - PromptPilot can optimize prompts for multi-modal scenarios, breaking down tasks into multiple steps and searching for optimal solutions [5]. - It enhances function call scenarios by optimizing both the triggering instructions and the descriptions of tools needed during task execution [5]. Group 3: User Accessibility - Users can easily integrate PromptPilot through an SDK, enabling automatic monitoring of "Bad Cases" and initiating a new round of prompt optimization [6]. - The platform standardizes the prompt engineering process, making it accessible for businesses and developers to focus on innovation in AI applications [6][7].