Stacks API Lab / Topic
Place AI within the application that surrounds it. Explore the relationship between model calls, user interfaces, data access, evaluation, tenant boundaries, and ongoing maintenance.
Place AI within the application that surrounds it. Explore the relationship between model calls, user interfaces, data access, evaluation, tenant boundaries, and ongoing maintenance.
Document what reaches the model, how it was selected, and what downstream components do with the output. A useful design names the permissions and operational boundaries around the call. In a SaaS application, those boundaries must remain understandable across tenant and user contexts.
Read the AI guide together with the starter-template review. Use a small representative feature to identify the context source, processing steps, evaluation examples, and recovery path. Record who owns each part and how a change is checked before expanding the feature to more users or more consequential actions.
Continue with the LLM Stacks API guide, or explore the complete stack atlas to connect this topic with the rest of the system.
A SaaS starter can shorten setup while introducing assumptions you must own. Evaluate one through a real product journey, tenant boundaries, accessible interfaces, operating costs, and the ability to maintain or replace its dependencies.
Read the guide : How to Evaluate a SaaS Starter Template Before Building on It
An LLM feature needs more than a model choice. Map the application, data, evaluation, and operating responsibilities first, then compare architectures using representative tasks and measured outcomes instead of unsupported performance promises.
Read the guide : Design an AI and LLM Stack Around Boundaries and Evidence