Software Documentation

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.

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.