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Jev does not document a single setting called “context isolation.” For decision-model requests, treat isolation as an application design practice: send only relevant evidence, put the judgment you want in the question, and keep trusted instructions and authority to take action in your application. The checklist below explains how to configure that separation without mistaking tidy JSON or a model response for a security boundary.
1. Choose a model that fits your need for stability
The Jev Models page describes jev-1.13 as a pinned model ID and jev-latest as a rolling alias. The page says the two share the context window, price, and request shape; the distinction is whether the build behind the ID can change. It also says that omitting model selects jev-1.13. See Jev’s Models documentation.
| Model choice | Build behavior | Useful when |
|---|---|---|
jev-1.13 |
Pinned, according to Jev’s Models documentation. | You need a stable model ID for evaluation, caching, or comparing runs over time. |
jev-latest |
Rolling alias; the build behind it can change, according to Jev’s Models documentation. | You want to adopt updates automatically and can accommodate behavior changes. |
Record the response’s model_version alongside each decision, particularly when calling jev-latest. That record helps you investigate a change in outputs; it does not itself make runs reproducible.
2. Keep the submitted state focused and inspectable
Start with the smallest state that contains the evidence needed for the decision. Add relevant policy text and definitions only when they are needed to interpret that evidence. For large histories, filter or retrieve relevant passages rather than sending the entire record “just in case.” When using retrieved material, identify the passage and its source so a reviewer can see what informed the result.
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Jev’s independently maintained manual, marked Official Checked 2026-09-21 for Jev 1.13.0, puts the distinction plainly: “State is the material Jev evaluates. Keep facts in state and judgments in questions.” See the Jev State Guide.
Choose a state shape that matches the evidence
| Representation | Best fit | Example use |
|---|---|---|
| String | One short passage. | A single customer message or policy excerpt. |
| JSON object | Facts with distinct meanings. | ticket_message, account, and relevant_policy as separate fields. |
| Array | An ordered sequence or a set of candidate passages. | A series of messages or retrieved policy excerpts. |
Descriptive field names make it easier to ask about the intended evidence. They do not make irrelevant fields useful, nor do they guarantee that Jev will follow a hierarchy embedded in the state.
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3. Separate evidence, requested judgment, and trusted instructions
Put facts and source material in state. Put the decision you want Jev to make in the question, and define allowed outcomes in the criteria or trusted instructions. For example, a question can ask whether a ticket meets a named policy condition, while the state contains the ticket text, the relevant verified account facts, and the policy passage.
Keep user-provided text inside a data field; do not concatenate it into trusted instructions. Preserve the distinction between a user’s claim and a verified account fact, and retain dates, units, identifiers, and source attribution. If a required field is missing, make that absence explicit rather than filling it by inference.
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Structural separation improves clarity, but it is not a security boundary. It does not prove that a classifier resists prompt injection or reliably obeys an instruction hierarchy. Do not grant model output independent authority to approve, delete, pay, or otherwise trigger consequential actions.
4. Check documented limits before building requests
Jev’s Models page, accessed 2026-10-04, lists the following limits. Treat them as documented values from that page, not guarantees for every provider or future deployment; check the active model and API before relying on them.
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| Documented limit | Value |
|---|---|
| Context window | 32,000 tokens |
| Maximum state size | 100,000 characters |
| Maximum questions | 20 |
| Maximum instruction length | 1,000 characters |
| Choice labels | 2–24 |
| Score tiers | 2–10 |
| Daily decisions per key | 10,000 |
These are capacity limits, not accuracy or safety measurements. A request that fits within them can still contain irrelevant, contradictory, or insufficient evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Test cases that expose weak context design
Before using a decision flow operationally, test contradictory, empty, and very long inputs. Also compare answers with and without irrelevant history: if adding it changes the result, inspect for mixed time periods, conflicting facts, or incompatible instructions. Ensure the request still has enough evidence for the judgment when irrelevant material is removed.
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- Contradictory evidence: distinguish which source is a user claim and which is verified, and preserve dates so a reviewer can tell whether facts refer to different periods.
- Empty or missing fields: represent the absence directly and specify how the decision should handle it; do not let the model silently invent a value.
- Very long input: check what your filtering or retrieval step keeps, truncates, or drops, and verify that the evidence necessary for the decision survives.
- Irrelevant history: test whether it changes the judgment. If it does, narrow the state and inspect for conflicts before deployment.
6. Keep action controls in application code
Jev’s API guide shows examples for a prompt-injection guard and an agent risk check, but examples are not a security certification. Application code should constrain which actions are available and verify relevant conditions or outcomes before acting on a model judgment.
The context-filter route shown by jevtypesafe.org’s hosted API guide illustrates keep, truncate, and drop decisions. That guide identifies itself as an independent third-party tool and says it is not affiliated with TypeSafe or Cloudflare. Confirm endpoint ownership, the account you would use, and current terms with the intended service before integrating it.
7. Review client and provider assumptions
The realbogart/jev client README reports provider-enforced limits and notes that pinning the model matters when thresholds depend on model behavior. Treat a client README as implementation guidance, not as a replacement for current documentation from the provider you actually call. A community cookbook can offer implementation context, but it is not official authority: nexibeo/jev-cookbook.
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