Articles
The article delves into the emerging field of Prompt Engineering, emphasizing its importance as the key to effectively leveraging Large Language Models (LLMs). It begins by explaining the basic concepts of prompts and Prompt Engineering, highlighting prompts as the bridge between humans and machines, and Prompt Engineering as a systematic approach to design, test, and optimize prompts. It then analyzes the four core components of high-quality prompts: background information, instructions, input data, and output indicators. Following this, it proposes seven golden design principles, including being clear and specific, assigning roles, providing examples, breaking down tasks, using delimiters, setting clear constraints, and iterating continuously, to guide readers in constructing effective prompts. The article also introduces advanced techniques such as Chain of Thought (CoT), ReAct, self-consistency, and structured prompt frameworks like RTF, CO-STAR, and CRITIC. Finally, through two practical cases, "Taobao XX Business Digital Intelligence Agent" and "Deep Learning Research Paper Reading", it details the core value and application models of Prompt Engineering in addressing key business challenges, enhancing data insights, and facilitating efficient learning, demonstrating its significance and practical value in enterprise-level AI applications.
This article explores how Alibaba Group addresses stuttering and unnatural text display in streaming output in the MNN LLM Chat application when deploying local LLMs on iOS clients, through a three-layer collaborative optimization. The article first analyzes core problems such as mismatch between model output and UI updates, frequent refreshing leading to performance bottlenecks, and a lack of visual animation. To this end, the author proposes and elaborates on a layered solution: `OptimizedLlmStreamBuffer` for intelligent buffering, `UIUpdateOptimizer` for UI throttling and batching, and `LLMMessageTextView` for typewriter animation. These optimization practices significantly improve the fluency of text output in local LLM applications, making the interactive experience close to the 'silky smooth' effect of online services such as ChatGPT.
This article explores the core issues confronting e-commerce platforms' SPU (Standard Product Unit) systems, including the strong coupling between definitions and categories, inconsistent data quality, and inefficient review processes. To overcome these challenges, the article highlights the specific practices and significant achievements of LLM technology in SPU data production, intelligent review, data governance, and application scenarios such as product hosting and atomic category SPUs, including algorithm-generated SPUs, machine review assistance, and outsourced review optimization. By implementing LLMs, the platform has significantly enhanced the uniqueness, accuracy, and timeliness of SPU data while reducing labor costs. The article concludes by looking ahead to a comprehensive SPU lifecycle upgrade driven by LLMs by 2025, aiming to establish a more efficient, accurate, and intelligent SPU data ecosystem.