Start with the decision, not the model
Most failed AI features begin with a model search. Strong teams begin with a decision: what should change for the user if the system is right, and what should happen if it is wrong.
Write the acceptance criteria as product behavior. For example, “suggest three follow-up actions with a confidence threshold” is clearer than “integrate an LLM.”
- Define the user-visible outcome first
- List failure modes and fallbacks before choosing a model
- Decide what must stay deterministic versus probabilistic
Keep a thin, measurable interface
Wrap model calls behind a stable interface with typed inputs and outputs. That boundary lets you swap providers, add caching, and run evaluation suites without rewriting product code.
Instrument every call with latency, token cost, and quality scores. If you cannot measure quality, you cannot improve it.
type SuggestActionsInput = {
context: string;
maxActions: number;
};
type SuggestActionsResult = {
actions: string[];
confidence: number;
model: string;
};Ship with guardrails
Reliable AI is boring by design. Rate limits, prompt versioning, content filters, and human escalation paths should be default infrastructure, not last-minute patches.
Treat prompts and evaluation datasets as versioned artifacts. When quality regresses, you need to know what changed.
