Prompt Analytics : Identifying and fixing content gaps

Fachvortrag
Künstliche Intelligenz (KI) in der Technischen Kommunikation

Dr Selvaraaju Murugesan
- Document360

Inhalt

Given the prominence of chatbots across many documentation sites, it is important for technical writers to harness the power of analytics by analysing all prompts in a privacy-centric way. Unlike lexical search analytics whereby technical writers only get list of keywords, prompt analytics provide a clear intent of the “ASK”. Analysing all prompts entered in your chatbot helps technical writers to quickly identify knowledge gaps, address trending topics, and fix the documentation gaps. Moreover, understanding prompt drift helps technical writers how customer behaviour is changing and it help them fix content accordingly.

Das lernen Sie

Key takeaways are

Introduction to Prompt analytics
- lexical search keywords vs prompts
- Characteristics
Types of prompt analytics
- Identify knowledge gaps
- Identify trending topics
Fixing content gaps
- Creaing and updating knowledge

Vorkenntnisse

Nil

Referent:in

Dr Selvaraaju Murugesan

Document360

Zum Profil

Biografie

Selvaraaju (Selva) Murugesan received the B.Eng. degree in Mechatronics Engineering (Gold medalist) from Anna University in 2004 and Ph.D. degree in Computational mathematics from LaTrobe University, Australia.

Selva is working as a Head of Data Science at Kovai.co, SaaS startup located in London. Selva is passionate about technical documentation, technical writing, data analytics and Artificial Intelligence.

Selva leads a technical documentation team and data science team to bring Artificial Intelligence product features into Document360, knowledge base platform.

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