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Use a backticked dot-and-index path in a Jev question to identify the exact field in structured state that the model should judge—for example, ticket.messages[0].text refers to the text in the first message. The path clarifies what evidence you mean; it does not authorize access, validate that the field exists, isolate tenants, or control what your application does with Jev’s result. Those protections belong in application code.
What a dot-and-index path refers to
TypeSafe describes state as “the content you ask a System One model to evaluate.” In Jev, a request evaluates one supplied state against one or more questions, which are evaluated independently. State can be a string, a structured JSON object, or an array of text values; TypeSafe advises using an object for most requests so the parts and their relationships remain clear. See TypeSafe AI’s State documentation.
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For a question about a particular field, Learn Jev documents backticked dot-and-index paths such as ticket.messages[0].text and order.charges[0].status. Each segment follows the supplied object’s shape: dots identify named fields, and brackets select an array index. A path is a reference to a location in the state, not a separate query or access-control mechanism.
Keep the evidence in state and the judgment in the question. If a judgment depends on a ticket, an order, and a policy, represent them as related pieces of one structured object and name the relevant evidence in a bounded question. Learn Jev’s path guidance is at Learn Jev: State.
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A safe implementation pattern
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Build state only from authorized records
In application code, select records the current user or workflow is permitted to use before constructing the Jev request. Use descriptive field names that make the object understandable to developers reviewing the question. Do not rely on a path, tenant identifier, or request identifier to prevent unauthorized data from entering state.
-
Filter out irrelevant context
Include evidence that can affect the judgment and omit material that cannot. TypeSafe’s failure-mode guidance for
jev-1.13recommends reducing indirection and filtering irrelevant context. Simpler, reviewable state can make the intended evidence easier to identify and a mistaken reference easier to debug; the cited guidance is not a quantified accuracy or performance guarantee. -
Match the path to the actual data shape
For example, if state contains a
ticketobject with amessagesarray whose message objects have atextfield,ticket.messages[0].textnames the first message’s text. Check in code that the array index is in bounds and that required fields are present. A plausible-looking path does not prove that the location exists or contains the value you expect.Free tools Windows power users keep installed
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Ask one clear, bounded question
Make the path’s role explicit and keep the requested judgment literal. Separate distinct decisions rather than combining multiple policy questions in an indirect instruction. Keep exact arithmetic, counting, and date comparisons in code instead of asking Jev to serve as a deterministic rules engine.
-
Assume state text may be adversarial
User messages, documents, and retrieved passages can contain instructions or misleading framing that attempt to steer a model judgment. TypeSafe’s
jev-1.13warning, reproduced in Learn Jev, is: “State is data, and jev-1.13 does not treat it as hostile by default.” Treat text as evidence to evaluate, not as trusted instructions for the application. Test prompt-injection cases, and do not let a single Jev result authorize an irreversible action. The guidance is version-specific; TypeSafe last reviewed the underlying failure-mode list on 2026-09-17. See Learn Jev: Failure Modes. -
Validate the result and enforce policy in code
After receiving Jev’s typed result, validate its shape and allowed values. Apply permissions, business constraints, escalation thresholds, and action rules in ordinary code. A model judgment can inform a workflow, but application logic must decide whether the requested action is allowed.
Example: identifying a refund request
state = {
"ticket": {
"messages": [
{"from": "customer", "text": "Please refund the duplicate charge."}
]
},
"refund_policy": "Duplicate captured charges are eligible for a refund."
}
question = {
"type": "noul",
"instructions": "Does `ticket.messages[0].text` request a refund?"
}This illustrates the documented path style and keeps supplied evidence separate from the judgment. It is illustrative code, not a tested Jev call or a claim about a particular returned probability. Before issuing a refund, the application still needs to verify the customer’s authorization and the charge records, apply the policy, and decide whether the action is permitted.
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Which layer owns each responsibility?
| Layer | Responsibility |
|---|---|
| Application code before the request | Authorize and retrieve records, isolate tenants, validate and scope inputs, and minimize state to relevant evidence. |
| Jev question | Use a clear path to identify supplied evidence and ask a bounded judgment about it. |
| Application code after the result | Validate the result, apply deterministic rules and permissions, and control whether an action occurs. |
The path notation does not create a security boundary. Jevbox’s security documentation shows permission checks implemented around that application’s retrieval; it is an example of application-level controls, not a guarantee provided by Jev. See Jevbox security documentation.
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What paths do—and do not—establish
- They make a reference explicit: the question can point to a named field and array element in the state supplied to Jev.
- They do not grant or restrict access: tenant isolation and authorization must be enforced while retrieving and preparing state.
- They do not validate the data: the application must check shape, required fields, and array bounds.
- They do not neutralize hostile content: test user-controlled text and keep consequential decisions under application control.
- They have no established universal depth limit or performance statistic: the cited material does not establish an optimal maximum nesting depth or a measured effect on accuracy, latency, cost, or security. Keep paths understandable and structures reviewable; if your team sets a numeric depth limit, treat it as a local convention rather than a Jev requirement.
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