Refactoring content is a must, given the proliferation of GenAI tools in the market. Most of the GenAI vendors have scrapped the internet to train their Large Language Model (LLM).Public knowledge vendors likely already use public GenAI bases, and customers may turn to tools like ChatGPT for answers.
If you have implemented a GenAI-powered search engine on top of your knowledge base, refactor content for GenAI agents, considering both human readers and GenAI needs.
This blog provides practical guidelines for undertaking content audits to refactor the content suitable for GenAI-based agents and tips on balancing the needs of human readers and GenAI agents. Prioritize public-facing knowledge bases for content audits to ensure customer satisfaction.
Top 5 Guidelines for Refactoring Content for GenAI
Rule1: Content hierarchy
The content hierarchy assures that content is well-researched and well-written considering readability and comprehensibility. It matters for GenAI-based agents to understand the holistic perspective and how the sections are interrelated. During the content audit, check whether the data is structured and presented adhering to H1 – H6. Technical writers must focus on documentation content where this semantic rule is followed. Best practices in structuring content as per hierarchy help GenAI-based agents, human readers, and content-scraping bots from search engines. Information architects can help restructure the content.
Rule 2: Content length
GenAI-based agents such as assistive search and chatbots need more textual data to understand the context better, and this enhances their ability to answer many questions from human readers. Having minimalistic content does not suit the characteristics of GenAI! The content should be revised such that more content is added. Explaining simple concepts more elaborately helps the GenAI to understand the semantic structure and build domain expertise of your knowledge base content.
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