Webinar - Stop Writing, Start Governing: The Tech Writer’s Role in the AI Stack
- Description
- Curriculum
- Reviews
In this session, Mayur Agrawal shares a case study from a massive “Knowledge Cleanup” initiative where archiving 1,000+ legacy documentation sets directly improved the accuracy of an enterprise AI search system.
We will move beyond the hype to discuss the engineering reality of AI adoption:
● Why “Chunking Strategy” matters more than “Prompt Engineering.”
● How to pivot your team from “Content Creators” to “Data Governors.”
● The math behind Retrieval Logic and why structured content (DITA/Markdown) is the only way to safeguard against hallucinations.
Stop writing for humans. Start architecting for agents.
Your Vector Database is a Trash Can
Everyone wants a custom GPT. Enterprise leaders are racing to ingest decades of documentation into Vector Databases to power RAG (Retrieval-Augmented Generation) systems. But there is a hidden problem: Legacy Debt.
When you feed an AI model 15 years of unstructured, conflicting, or outdated content, it does not magically find the "truth"—it hallucinates. In the age of AI, "more content" is a bug, not a feature.
