AI Instruction Adherence Playbook
Assesses whether AI-generated responses follow system instructions, brand tone, and formatting requirements. Flags deviations to ensure consistent and controlled AI behaviour.
Start building
Deploy this kit stack into your workspace. Customize bricks, scoring, and outputs to match your team.
Without this playbook
Most teams handle ai instruction adherence through scattered call reviews, manager opinion, and isolated examples. Without a shared operational definition, the signals stay inconsistent and difficult to act on across volume.
With this playbook
A shared, repeatable lens for ai instruction adherence - with structured outputs you can route into coaching, reporting, and workflow automation. Every conversation produces evidence, not just opinions.
Built for
AI product managers, ML engineers, and trust & safety teams
When teams use it
- Model evaluation and release gates
- Governance review and policy enforcement
- Safety and accuracy monitoring
Knowledge base
Supporting materials
The kits in this playbook work best when backed by reference materials that ground the evaluation. Upload these into your workspace knowledge base to improve accuracy and relevance.
Learn more about Knowledge BasesSystem prompts and instruction sets for each AI agent
Brand voice guidelines and tone documentation
Formatting requirements and response templates
Known deviation patterns and edge cases
AI behaviour governance policies and review procedures
In practice
How teams use these outputs
The structured outputs from this stack integrate into your existing workflows. Use them wherever you need repeatable, evidence-based signal from conversations.
Model evaluation and release gates
Governance review and policy enforcement
Safety and accuracy monitoring
AI agent performance benchmarking
Get started
Deploy this playbook in your workspace
Customizing creates a workspace-owned draft with this playbook's full kit stack. Adjust bricks, scoring, and outputs to fit your team, then publish when ready.