Complete Jev setup guide

Jev is an AI decision model designed for bounded decision-making tasks such as classification, selection, scoring, routing, filtering, and yes/no judgments.

Use Jev when you need AI to make a specific, constrained decision.

Good examples:

• Classify a support ticket
• Choose the correct department
• Score urgency
• Detect refund requests
• Filter important messages
• Route an AI agent
• Decide whether human review is needed

Jev works best when the possible decisions are clearly defined.

• Create an account with the service providing Jev access.
• Complete your account setup.
• Open the API settings.
• Create an API key.
• Store the API key securely.
• Never expose it publicly.

Depending on available integrations, Jev can be incorporated into:

• API workflows
• Python applications
• JavaScript applications
• Command-line workflows
• AI coding agents
• Agent Skills
• Automation systems
• Custom AI agents

For CLI-based workflows, make sure your environment meets the CLI's current requirements.

Typical requirements include:

• Python 3.9 or newer
• Terminal access
• curl
• tar
• Checksum utilities

Verify requirements against the current CLI release before installing.

Install the current Jev CLI release.

After installation, verify it with:

jev --version

If the command is unavailable, confirm that the installation directory is included in your system PATH.

Configure your API credentials through the CLI:

jev auth set

Enter your API key when prompted.

Then inspect your authentication status:

jev auth status

Verify that authentication works:

jev auth check

For servers, containers, CI/CD pipelines, and automated systems, store credentials using environment variables or a dedicated secret manager.

• Never hardcode API keys directly into your application.
• Never commit credentials to Git.

Jev can serve as a specialized decision layer inside a larger AI agent.

• The agent can collect information.
• Jev can evaluate a bounded decision.
• Your application can then execute the appropriate action.

This separation keeps decision-making and execution easier to control.

Start by deciding exactly what Jev needs to determine.

Common patterns include:

Make the available choices explicit.

Example:

• Sales
• Billing
• Technical Support
• Refund
• Spam
• Other

Avoid overlapping categories wherever possible.

Every realistic input should have an appropriate outcome.

Adding an "Other" or "Unknown" option can prevent ambiguous inputs from being forced into an incorrect category.

Avoid:

Is this message useful?

Prefer:

Does this message contain specific technical, product, or industry information?

Clearly define what qualifies and what does not.

Do not automatically send entire documents, databases, or conversation histories.

Provide only the information required for the decision.

This can improve:

• Speed
• Cost
• Privacy
• Predictability
• Debugging
• Evaluation

Example:

Input

Customer message

Decision

What type of request is this?

Choices

Sales, Billing, Technical Support, Refund, Other

Output

Selected category

Your application can then determine what happens next.

Jev might determine:

This is a refund request.

Your application should separately determine whether it can:

• Issue a refund
• Contact the customer
• Modify an account
• Update a database
• Trigger another workflow

This separation reduces the consequences of an incorrect classification.

When confidence information is available and calibrated for your workflow, you can define different handling rules.

Do not assume a confidence score automatically represents a reliable probability of correctness.

Not every decision needs full automation.

A practical workflow can support:

• Automatic acceptance
• Automatic rejection
• Human review

Use stricter safeguards for high-impact decisions.

Test:

• Clear cases
• Negative cases
• Ambiguous cases
• Edge cases
• Missing information
• Conflicting information
• Very short inputs
• Long inputs
• Unusual wording
• Adversarial inputs

Do not evaluate the system using only ideal examples.

Build a collection of representative inputs with known expected outcomes.

Run Jev against this dataset whenever you modify:

• Prompts
• Decision criteria
• Categories
• Application logic
• Model configuration
• Workflow architecture

Track metrics such as:

• Accuracy
• Precision
• Recall
• False positives
• False negatives
• Human-review rate
• Latency
• Cost
• Failure rate
• Performance by category

The appropriate metrics depend on your use case.

Jev can help determine:

• Which agent should respond
• Which tool should be used
• Which workflow should run
• Which department should receive something
• Whether escalation is necessary
• Whether human intervention is required

Potential classification workflows include:

• Support tickets
• Emails
• Documents
• Leads
• User requests
• Feedback
• Content
• Logs
• Product issues

Jev can also support bounded scoring tasks.

Examples:

• Urgency
• Priority
• Relevance
• Lead quality
• Review priority
• Content quality
• Risk indicators

Clearly define what each score means.

A typical workflow can be:

• Receive input
• Prepare relevant context
• Send decision request
• Receive structured decision
• Validate response
• Apply business rules
• Execute permitted action
• Log the result
• Escalate uncertain cases

Never blindly trust model output.

Validate:

• Output format
• Allowed values
• Required fields
• Data types
• Missing responses
• Unexpected responses
• API failures
• Timeouts

Your application should gracefully handle:

• API errors
• Authentication failures
• Rate limits
• Timeouts
• Invalid responses
• Missing data
• Network problems
• Unexpected classifications

Use retries only where appropriate.

Record enough information to understand system behavior.

Useful fields include:

• Decision requested
• Decision returned
• Confidence information, when available
• Timestamp
• Model configuration
• Latency
• Final action
• Human override

Avoid unnecessarily logging sensitive information.

Continuously monitor:

• Incorrect decisions
• Unexpected inputs
• Latency
• API failures
• Costs
• Human overrides
• Category distribution
• Decision drift

Performance can change as real-world inputs change.

Never:

• Commit it to GitHub
• Put it in frontend JavaScript
• Include it in screenshots
• Expose it in browser code
• Publish it in documentation
• Share it publicly

Use environment variables or a secure secret manager.

A decision model should not automatically receive unlimited permissions.

Only give downstream systems the minimum permissions necessary to perform their intended actions.

High-impact actions should require stronger validation or human approval.

Good first projects include:

• Email categorization
• Support routing
• Lead classification
• Document categorization
• Content tagging
• Intent detection
• Priority classification

Once performance is validated, gradually expand the workflow.

• Jev access
• API credentials
• API, CLI, or supported integration
• Clearly defined decision
• Bounded answer space
• Relevant input
• Output validation
• Application logic
• Human fallback
• Evaluation dataset
• Logging
• Monitoring
• Security controls