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Decision APIs implementation guide: TypeSafe Jev, OpenAI Decisions, and Gemini Decisions

By ChatGPT Deep Research

As of November 12, 2026, all three providers now expose APIs specifically designed for machine-consumable judgments rather than free-form generation. The common shape is: provide shared evidence, define one or more bounded questions, receive typed values plus probabilities and, for some question types, confidence. The important implementation differences are in their schemas, supported modalities, model/versioning strategy, question limits, failure modes, and SDK ergonomics. The sections below use only official TypeSafe, OpenAI, and Google documentation, beginning with the pages specified in the request. ^{1, 2, 3}

Architecture and provider selection

A useful mental model is that a Decisions API is not a general-purpose chat endpoint with a JSON output constraint. It is an inference primitive where **your application owns the d

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Car Doctor AI Stack

CarDoctorAI.com | Car Problem? Get Answers.

AI Chat

• Vercel AI SDK for streaming chat, tool calls, artifacts, and usage data
• OpenAI models for chat, reasoning, artifacts, and mechanic web search
• TokenLens for model usage enrichment

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Boat Gen

BoatGen.app | Add style to a photo of your boat

Stack

AI Image Editing and Generation

• BFL Flux Pro and derivative models
• Replicate for AI model hosting and inference

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absl-py==0.10.0
cachetools==4.1.1
certifi==2020.6.20
chardet==3.0.4
fsspec==0.8.3
future==0.18.2
google-auth==1.22.1
google-auth-oauthlib==0.4.1
grpcio==1.32.0
idna==2.10

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Quick Start

Quickly deploy a Python-Flask app to Heroku from scratch with this tutorial:

• http://blog.shea.io/lightweight-python-apps-with-flask-twitter-bootstrap-and-heroku/

Bonus: nice styling with Bootstrap.

The above is one of the best tutorials I've seen - gives you exactly what you want and nothing you don't need. However, it may be just a tad outdated, so you may have to look at updated docs on heroku.

Examples

Here is one small Flask app that I built:

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library(tibble)
library(dplyr)

## sample data
df <- frame_data(
    ~x, ~y,  ~z, ~x1, ~x2, ~x3, ~y1, ~y2, ~y99, ~z99, ~zz,
    99, 2,  3.6, 99, 2,  3.6, 99, 2,  3.6, 99, 2,
    99, 2,  3.6, 99, 2,  3.6, 99, 2,  3.6, 99, 2,
    99, 2,  3.6, 99, 2,  3.6, 99, 2,  3.6, 99, 2
)

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TMUX basic commands

Scroll down for more

tmux ls

tmux new -s mysession       # create new

tmux attach -t mysession    # or attach recent:  tmux a

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/* macro to convert variables to indicators*/
/* outputs dataset "df" with patid and the indicator only for easy later merge*/
/* v = var for indicator // n = name of new var(use same prefix with consecutive #s!!) */
%MACRO INDX(v=, n=);
   data df;
       set bmd;
       if &V > . then tt = 1; else tt =0;
       rename tt=&N;
       label
           &N = "&V 0/1"