May 21

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.

As Generative AI evolves from passive chatbots to active agents, the role of the technical writer is shifting from content creator to information architect. This session introduces a cutting-edge, privacy-first workflow: the Automated Documentation Reviewer, built on the Model Context Protocol (MCP).

We will demonstrate a “Docs-as-Code” pipeline where an autonomous agent monitors a documentation repository, acting as a tireless preliminary editor. Unlike cloud-dependent tools that risk IP leakage, this workflow prioritizes Data Sovereignty. We will use various local models—such as DeepSeek R1 and other frontier labs’ models—to run locally, ensuring sensitive technical data never leaves the corporate firewall.

The architecture is designed for edge deployment with optional integration of larger models hosted on company servers through accessible API endpoints.

This session provides a guideline for enterprises seeking high-speed, compliant, and secure documentation engineering—eliminating reliance on third-party APIs while advancing technical writing standards.

In multi-agent application development, traditional approaches that rely on text files (such as .md or .txt) to aggregate skills, commands, and interaction logic are often constrained by model limitations in quantization and parsing. This can lead to character loss or execution errors.

In this presentation, I will present a recently explored writing engineering approach: storing agent skills, commands, and interaction logic in structured databases, and driving agent interactions through database queries and operations. This approach enables a low-cost, highly stable execution environment. It ensures skill integrity and traceability, improves model robustness in complex task execution, and provides a scalable, engineering-oriented path for large-scale agent-based writing applications.