AI, Automation & the Future of Content

In 2025, with Huawei’s Ascend and Kunpeng platforms fully open-sourced—supporting users in deep customization and independent development, and jointly building an ecosystem—we have established an open-source-oriented information system and intelligent writing capabilities. Through documentation system design, writing mechanisms, and the definition and construction of open collaboration, while also introducing AI to enable intelligent document generation, quality inspection, and gating, we have significantly improved the quality and efficiency of open-source information development, meeting the needs of developers in co-building the ecosystem. Among these, the practical experience in AI-based intelligent document generation, quality inspection, and gating can serve as a reference for documentation efforts across various open-source communities.

Despite the rapid iteration of high-tech products driven by LLMs, the methods for creating, maintaining, and consuming the accompanying documentation have struggled to keep pace. Traditional methods may improve efficiency to some extent, but when dealing with complex products, documentation professionals still need to deeply understand product technical details and vast documentation structures to accurately locate and modify content, leading to significant bottlenecks in documentation maintenance.

To address this challenge, we have developed an AI-powered multi-agent solution for technical documentation, leveraging the powerful text generation capabilities of LLMs, semantic search technologies through embedded models, and agents with proactive thinking and execution abilities. This solution addresses pain points in document synchronization, review, proofreading, and operational maintenance, pushing these processes toward automation and intelligence. As a result, documentation transitions from a passive, static asset to an “active” resource capable of self-reviewing and continuously evolving.

As AI reshapes the landscape of technical writing, the issue of “factual hallucinations” in large language models (LLMs)—such as fabricated content, misattribution, and over-editing—has become a key barrier to their scalable application in high-accuracy scenarios.

This presentation proposes a new role paradigm for technical writers evolving into “Model Context Engineers.” Rather than simply writing text, this role involves systematically constructing, optimizing, and managing the trusted context that models rely on for generation.

This presentation provides an in-depth analysis of the underlying causes of hallucinations in technical documentation generation and defines the competency model and methodology for this new role. It introduces a three-tiered “Knowledge-Generation-Validation” anti-hallucination framework, detailing how to systematically build, optimize, and manage the input context for models, along with a feedback mechanism to create a closed-loop approach for mitigating hallucinations.