How to Build an Internal AI Playbook with Multiple Models
A simple framework for documenting which model to use for which job so teams stop guessing and start routing work intelligently.
# How to Build an Internal AI Playbook with Multiple Models
Teams lose a surprising amount of time to one repeated question: “Which model should I use for this?”
The answer should not live in Slack folklore. It should live in a playbook.
What your playbook needs
Keep it simple. For each common task, document:
- Recommended model
- Backup model
- Prompt template
- Quality risks
- Cost sensitivity
That is enough to make adoption much smoother.
Start with the top ten recurring tasks
Do not document everything. Start with the tasks people repeat every week.
Examples:
- Draft a client update
- Summarize a meeting
- Review a contract excerpt
- Generate SQL or code snippets
- Compare two strategic options
Write task-first guidance
Do not organize the playbook by vendor. Organize it by job.
This matters because users care about outcomes, not model branding.
Review the playbook monthly
Model quality and pricing change. Your playbook should too.
A monthly review helps you catch:
- A cheaper model that got good enough
- A premium model that is no longer worth it for a task
- Prompt patterns that need cleanup
Why a multi-model workspace helps
A playbook is much easier to follow when the team can access the recommended models in one place. Otherwise the document says one thing and the tool stack pushes people somewhere else.
That mismatch is why many AI rollout efforts stall.
The goal
A strong internal AI playbook makes model choice boring. That is good. It means the team can focus on the work instead of guessing which tab to open.
Run this decision in Compare mode
Land on a prefilled comparison instead of a blank box, then adjust the prompt for your exact use case.
Open prefilled comparison