In this first session of the Let’s Talk Knowledge Engineering series, Ben Taylor, Rainbird CTO and co-founder, is joined by Eliza Filby, Senior Knowledge Engineer, to introduce the discipline of knowledge engineering and explain why it matters now.
Together, they outline how organisations can capture expert reasoning, structure it into transparent models, and use those models to power AI systems that are consistent, auditable, and explainable. The session also includes a live Rainbird Studio demonstration using a UK tax split-year example, showing how symbolic AI works in practice and how expert knowledge can be turned into a working reasoning model.
You can register for the remaining sessions in the series here.
What you’ll learn
- What knowledge engineering is, what it is not, and how it differs from data science, software engineering, prompt engineering, and spreadsheet-style rule building.
- Why knowledge engineering is becoming more important as organisations look for ways to move AI from proof of concept into trusted production use.
- What a Knowledge Engineer does day to day, including elicitation, modelling, testing, refining, and deployment.
- How symbolic AI creates transparent, deterministic, and auditable reasoning, and how it can work alongside generative AI in modern enterprise systems.
- How Rainbird Studio supports the practical work of knowledge engineering, from building graphs to testing models and explaining outcomes through evidence trails.
Resources shared in the webinar
- Rainbird Studio Community Edition: Experiment, model, and bring decisions to life, visit app.rainbird.ai
- Rainbird Academy: Learn the foundations of explainable decision intelligence, visit academy.rainbird.ai
- Rainbird Forum: Ask, discuss, and shape the conversation, visit forum.rainbird.ai