Decision Playground
Ask a question. Get a decision you can inspect and govern.
Give it an input and an operational question. TheThinkLoop evaluates the outcome, calculates supporting factor checks, and applies review thresholds to determine whether to automate, review, or abstain.
Decision
Your decision will appear here
Pick an example or write your own input and question, then run it. You’ll get the outcome, the provider’s estimate, the evidence checks, and whether a person should review it.
Demo mode
The equivalent Decisions API call
The main question and each evidence check go out as one request against the same input. Read the tutorial.
import OpenAI from "openai"; // openai >= 7.30.0
const client = new OpenAI(); // reads OPENAI_API_KEY on the server
const decision = await client.decisions.create({
model: "gpt-6-luna",
input: "I was charged twice for my annual plan (order #48213). I only signed up once, 5 days ago. Please refund the duplicate charge.",
questions: [
{
type: "predicate",
name: "decision",
instructions: "Should this refund request be approved under our 30-day refund policy?"
},
{
type: "predicate",
name: "factor_1",
instructions: "The customer reports being charged more than once."
},
{
type: "predicate",
name: "factor_2",
instructions: "The purchase was made within the 30-day refund window."
},
{
type: "predicate",
name: "factor_3",
instructions: "The customer has received a previous refund."
}
],
});
// Answers come back in question order. A question can also be refused.
const [main, ...checks] = decision.answers;
if (main.type === "refusal") throw new Error("The model declined to answer.");
if (main.type === "predicate") {
console.log(main.probability); // 0–1 estimate that the condition is true
}
How to read a result
- Probability and confidence are estimates
- A predicate returns the estimated probability that your condition is true. Choice and score return a confidence value for the selected answer. Neither tells you how often the system is right. That is accuracy, and you can only measure it against labeled examples.
- Key factors are checks you define
- The Decisions API returns typed answers, not written reasoning. Each factor on the card is a separate yes/no question asked against the same input, in the same request.
- Human review comes from your thresholds
- The card compares the estimate with the review policy you set. Where those thresholds should sit depends on what each kind of mistake costs you. Calibrate them with your own examples.
Where to go next
Predicate vs choice vs score
Which question type fits which decision, with examples of each.
AI decision routing
Combining several answers into one routed action, with review and fallbacks.
Decision templates
Worked decisions for support and operations teams, ready to adapt.
Calibrate thresholds
Turn labeled examples into automation and review thresholds.