---
translation_of: jev-applications
lang: en
title: 'What Jev can be used for: from probability gates to computable belief models'
summary: This post brings together the theory and experiments of the previous two and proposes a division of labour. Jev estimates only the most basic probabilities, math does all combination and inference, and real data calibrates accuracy. With this split, a language model's knowledge can be turned into a probability model that is self-consistent, editable and explorable. It discusses how this differs from asking a language model directly, and several application areas.
---

The previous two posts, "[Probability gates: from bits to probabilities](/blog/probability-gates/)" and "[Probability gate experiments: can Jev's probabilities be used as they are?](/blog/probability-gates-experiments/)", covered the theory and the experiments. This post puts the conclusions together and looks at what Jev is suited for.

## What we know so far

The experimental findings come down to six points:

1. **Fast, cheap and batchable.** A call takes about 0.2 seconds on the server, and one request can ask a dozen or more questions with little interaction between them.
2. **The output is almost deterministic.** Asking the same question again barely changes the answer, so the errors are mainly systematic and repeated calls do not reduce them.
3. **Strictly complementary yes/no questions are mostly consistent.** The "yes" and "no" probabilities sum to within about 0.04 of 1 on average.
4. **Asking compound questions directly is unreliable.** Asking separately for A, B, A and B, and A or B, 81% of combinations violated the rules of probability, and "or" was systematically underestimated by about 0.10.
5. **The ranking is accurate; the values are pulled toward the middle.** On 33 census groups, Jev's estimated shares of high earners had a rank correlation of about 0.97–0.98 with the true shares, but low shares were overestimated. Calibrating with a small amount of real data cut the error from 0.119 to 0.042.
6. **Composing in steps does not automatically improve accuracy.** Errors in different directions cancel; bias in a shared direction passes straight through and can only be handled by calibration.

## A division of labour

These conclusions point to a division of labour:

- **Jev estimates only the most basic probabilities**: the probability of a single proposition, and conditional probabilities under stated conditions. Yes/no questions for judgments about one case, a scale for the share of a population, multiple choice for mutually exclusive categories.
- **Math does all combination, inference and sampling**: "and", "or", marginalization, Bayesian updating and sampling are computed by code according to the rules of probability, without asking Jev again.
- **Real data calibrates accuracy**: known outcomes correct Jev's systematic bias, calibrated at the level where the numbers are used.
- **A language model, a person or an existing proposition library supplies the structure**: which propositions exist and what depends on what. The dependencies can also be judged by Jev; see below.

![Sketch: division of labour](/blog/assets/jev-applications/division.en.svg)

With this split, consistency is guaranteed by the math and does not depend on Jev; accuracy depends on calibration and can be checked against data continuously.

## Building a large model from yes/no questions

Can yes/no questions alone determine a complete probability model over many propositions? Yes; there is an existing mathematical structure for it.

n yes/no propositions have 2ⁿ combinations, so describing the full joint distribution directly takes 2ⁿ − 1 numbers, far too many to ask one by one. A **Bayesian network** breaks it down with the chain rule: each proposition depends on only a few "parent" propositions, and the parameters needed are "the probability that this proposition holds, given a particular set of values for its parents". Each parameter is exactly one conditional yes/no question.

These parameters have a key property: they are mathematically independent of each other. Whatever value between 0 and 1 each one takes, together they always form a valid, consistent joint distribution. So parameters can be asked, calibrated and replaced one at a time, and consistency comes from the structure itself. The contradictions seen in the experiments only appear when the same quantity is estimated more than once.

If you ask many overlapping questions, such as single probabilities and the probabilities of pairs holding together, the answers may disagree. Then you can use the **maximum entropy method**: find the distribution that satisfies all the answers as closely as possible and assumes nothing beyond them. Mathematically it is a Boltzmann machine: each proposition has a bias, and each pair of propositions has a coupling strength.

This differs from how model parameters are usually obtained. The parameters of Boltzmann machines and neural networks are normally learned from large amounts of data or adjusted with reinforcement learning. Here Jev gives the probabilities directly, and code derives the parameters from them, with no training. The knowledge still comes from a trained model (Jev itself), but it is organized into a set of named, meaningful numbers that can each be checked.

## Most of the structure can also go to Jev

The structure has two parts: which propositions exist, and what depends on what.

Dependencies can be settled with yes/no questions. For every pair of propositions, ask "does A directly affect B?"; n propositions need n(n−1) questions. Fifty propositions need about 2,450 questions, and since they all read the same background, one request can ask a dozen or more, for a total of one or two hundred requests. Two things still need handling:

- **Indirect effects.** If A affects C through B, Jev may also answer yes to "does A directly affect C?". A further question, "given B, does A still affect C?", removes the redundant edge. This is how the PC algorithm for structure learning works, with Jev's judgment replacing the statistical independence test.
- **Cycles.** If both "A affects B" and "B affects A" are judged true, a Bayesian network cannot represent it. Asking "does A happen before B?" can settle the direction, or code can keep the direction with the higher probability.

Jev cannot come up with the propositions themselves, because it does not generate text. But it can choose from an existing library by asking, for each entry, "is this relevant to the question?". If a large enough proposition library is built for a domain in advance (done once, by a language model or by people), then at run time only Jev and math are needed: choose the propositions, settle the dependencies, estimate the probabilities and compute the conclusions. Such a system is fast and cheap at run time, and its results are reproducible.

## How this differs from asking a language model directly

Once the model is built, it can do several things that asking a language model directly cannot:

1. **Every answer is consistent with every other.** Ask the same model 100 different questions and all the answers come from one distribution, so they never contradict each other. Ask a language model 100 separate questions and the answers often disagree.
2. **What-if analysis is instant.** Set "suppose this has happened", and the probabilities of all other propositions update exactly by Bayes' rule, without calling any model again.
3. **You can see which judgments a conclusion depends on.** You can compute how sensitive the conclusion is to each parameter and list "this conclusion depends mainly on these judgments". If the user disagrees with one, they change that one and the conclusion updates.
4. **It can say how reliable a conclusion is.** Estimate the same parameter with several wordings and propagate the spread to the conclusion, which gives a range such as "62%, range 55%–70%" instead of a single number.
5. **It can give several outcomes.** Sampling from the joint distribution gives an internally consistent "possible world" each time, which a language model can then write up as a description. The probabilities of the outcomes all come from the same distribution rather than being made-up percentages.
6. **Build once, use many times.** A model with ten thousand parameters takes a few minutes and under a dollar to instantiate with Jev; after that every query is computed locally and returns instantly.

## Others have walked this path

Decision analysis developed a mature set of methods in the 1970s and 1980s: influence diagrams, value of information and sensitivity analysis, with Ron Howard at Stanford as a leading figure. It hit the same problem as Bayesian networks: every parameter had to be estimated by an expert, which was expensive and kept models small. Jev provides an estimation source that is cheap, always available and can be called tens of thousands of times. The methods already exist; what was missing was the source.

## Application areas

From easiest to hardest to put into practice:

1. **Business judgment pipelines.** Message routing, ticket triage, action approval, risk grading. Each step gives a calibrated probability, uncertain cases go to a person, and every judgment is recorded. This is the most direct use of Jev.
2. **Acceptance evaluation.** Check an AI system's output against acceptance criteria one by one, calibrate against human ratings first, then rerun all the examples automatically on every change.
3. **Explorable belief models.** A user poses a question such as "will this project be late by the end of the year?". A language model lists the key propositions and dependencies, Jev fills in all the conditional probabilities, and the interface shows a graph of propositions. Click any proposition to mark it as "happened" and the other probabilities update immediately; a side panel lists the judgments the conclusion depends on most and the parameters with the widest disagreement; and you can sample several possible outcomes. When the real result is known, it is fed back for the next calibration. This is different from a single Jev (ask one question, get one number) and from a language model (ask one question, get a paragraph): you explore inside a consistent model.
4. **Other uses**:
   - Diagnosis: at each step, choose the question that most reduces uncertainty, to find the cause with as few questions as possible.
   - Event tracking: follow the probability of an event, update it with each new piece of news, and show which item changed it.
   - Building probability models in natural language: a language model proposes variables and dependencies, Jev fills in the parameters, and a small amount of data calibrates them.

When serving clients in mainland China, sending data to a model abroad is a cross-border data transfer. A probability gate is an abstraction and can also be implemented with a domestic model plus calibration, keeping the same structure and tools.

## Limits

- **Mathematically independent does not mean independent errors.** All parameters come from the same Jev, so systematic bias is shared and calibration is required.
- **The structure can be wrong.** The propositions and dependencies a language model proposes are not necessarily right, and structural errors matter more than parameter errors.
- **Scale does not bring more capability.** However many parameters there are, they cannot exceed Jev's own knowledge; scale buys coverage, consistency and explainability.
- **Open questions lack calibration data.** Questions without known outcomes cannot be calibrated directly; they can only borrow calibration from similar resolved questions, and until then the probabilities are mainly useful for comparison and ranking.

## Next

1. Build a small Bayesian network with six or seven variables on the Adult data, with every parameter estimated by Jev, and compare it with the real data and with asking directly.
2. Build a prototype of an explorable belief model.
3. Continue the forward forecasting experiment to test calibration on open questions.

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Note: Jev was called and paid for per use through the Vercel AI Gateway. The author and Tenon AI have no business relationship with TypeSafe AI and have received no payment.