AI Master Podcast episode with Ondřej Chrastina (Developer Advocate at CKEditor) on AI-powered content editing. Learn how CKEditor 5 differs from CKEditor 4 (model-based architecture vs DOM), why it’s easier to extend with plugins, and how it supports collaboration, versioning, and track changes. We discuss CKEditor AI features for content ideation, drafting, rewriting, and review while preserving formatting, user control, safety, privacy, and GDPR. Practical insights on integrating LLMs into rich text editors, handling latency and hallucinations, prompt vs context engineering, multilingual challenges, and measuring AI productivity.
Released: Sun Dec 14 2025
This episode is also available on YouTube:
Future of Content Editing with AI
In this episode of the AI Master Podcast, I’m joined by Ondřej Chrastina, Developer Advocate at CKEditor and an experienced Developer Relations leader with over 12 years of hands-on and leadership experience in building outstanding developer experience for complex platforms.
Ondřej shares his professional journey — from QA engineering and software development, through building internal tooling and SDKs, to establishing and leading Developer Relations programs at companies such as Kentico, Kontent.ai, Ataccama, and now CKEditor. We talk about how deep empathy for developer pain points shapes better products, tooling, and documentation.
A large part of our conversation focuses on CKEditor 5 — its modern, extensible architecture and the challenges of using rich-text editors in today’s web applications. We explore how generative AI and large language models can meaningfully enhance content editing, turning editors into intelligent assistants rather than passive tools.
Ondřej explains practical AI use cases such as content suggestions, summarization, auto-formatting, review workflows, and AI-assisted collaboration. We also go deep into the technical and product challenges of embedding AI into editors: latency, context management, hallucinations, prompt engineering, safety, and maintaining full user control — especially in enterprise environments where privacy, data governance, and GDPR compliance are critical.
We also discuss the future of rich-text editors in an AI-driven world, the role of open source and community contributions, multilingual challenges, and which AI advancements (embeddings, RAG, multimodal models) are most exciting for the next generation of editor experiences.
To wrap up, we switch to a more personal perspective — continuous learning, staying motivated as a software engineer, productivity, travel, and a few lesser-known facts about Ondřej.
Key topics covered in this episode:
- Architecting AI-powered features in rich-text editors while preserving user control
- Integrating LLMs into editors: latency, context size, and hallucination challenges
- Prompt engineering patterns for editor-based AI workflows
- Privacy, GDPR, and data governance in enterprise-grade AI integrations
- The future of rich-text editors as intelligent, collaborative assistants