Explore reusable AI templates for business and technical tasks, understand how each one is designed to work, then adapt the model, instructions and capabilities for your organisation. Move from a useful starting point to a configured assistant without rebuilding the experience from the ground up.
Building a useful assistant involves more than choosing a model. Teams also need to define its purpose, instructions, generation behaviour, starting prompts and available capabilities. Repeating that setup for every use case slows experimentation and makes it harder to preserve configurations that already work.
IntraLLM AI Templates bring those decisions together in a reusable format. Teams can begin with an existing assistant pattern, inspect what it offers, adapt the configuration and move it into Chat as a practical working experience.
Search and filter a central library to find templates aligned with a task, business function or industry context.
Preview a template's purpose, conversation starters and enabled capabilities before deciding whether it fits the use case.
Change the model, system instructions, generation settings and capabilities while preserving a useful template as the starting structure.
Move through one connected workflow for selecting a model, preparing data,
running GPU training and putting the result to work.
Browse the full collection, search by name or purpose, or use tags to narrow the library by business and industry context. Each card introduces the template’s role and creator, helping users move quickly from a broad requirement to a more relevant starting point.
Open a template preview to review its purpose, conversation starters and enabled capabilities. This provides a clearer picture of the intended experience before a user launches it in Chat or decides to adapt the configuration.
Configure the template name, visual identity, link slug, base model, description and tags. These settings make the assistant easier to recognise, organise and access while connecting it to an available runtime model in the workspace.
Add system instructions and refine generation behaviour through temperature, token limits, stop sequences and sampling or penalty controls. Configure conversation starters, then enable the capabilities the experience requires—from file and image input to web search, generation, code interpretation, forms and citations.
Start Chat opens the selected template as the active assistant, carrying its identity and configured experience into the conversation. When a similar use case appears, clone the template and adapt the copy instead of rebuilding the setup. Templates can also be imported, exported or accessed through a copied link where deployment settings allow.
A template is more than a description card. It brings together the model, assistant identity, system instructions, response settings, conversation starters and enabled capabilities that shape an interaction.
When opened in Chat, that configuration becomes the starting experience users work with.
Model Training is part of the wider IntraLLM experience.
The outcome is not left isolated in a training environment:
it becomes a model that can be managed,
accessed and used through the platform.
Package an assistant’s purpose, identity, instructions, model settings, conversation starters and capabilities into a configuration that can be used repeatedly.
Build a growing library around business functions and industries, with search and tags helping users find the right assistant for each task.
Select an available runtime model for a specific template or inherit the workspace default to align assistants with deployment requirements.
Shape how each assistant responds through persistent system instructions and optional generation controls, including temperature, token limits, Top P and penalties.
Configure the interaction modes each assistant needs—including vision, file upload, web search, generation, code interpretation, forms and citations.
Clone templates for related use cases, import or export configurations, copy direct links and launch the finished assistant straight into IntraLLM Chat.
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