What is a small language model?

A small language model (SLM) is a lighter, more focused type of AI language model. It’s typically trained on smaller or more specialized datasets and has fewer parameters than an LLM. While it may not match the scale or flexibility of its larger cousin, an SLM is often easier to deploy and more efficient to run—especially when tailored for domain-specific tasks. 

What is a small language model used for? 

SLMs are ideal for scenarios where speed, control, or cost-efficiency is critical. They can support customer self-service portals, enrich knowledge bases, enable smart search across a content hub, or assist in tagging and categorising modular content. When embedded in systems like a headless CMS or decoupled architecture, they contribute to responsive, scalable digital experiences. 

Why is a small language model useful? 

SLMs bring the benefits of AI without the overhead. They’re especially valuable in use cases involving structured content management, content reuse, or even retrieval augmented generation (RAG) within a secure environment. For content teams looking to dip their toes into agentic AI or build intelligent automation into existing tools, SLMs offer a pragmatic and focused path forward.