JEV LLM / HE JEV

JEV LLM connects language with intent.

JEV LLM frames intuitive thinking through language. Describe a goal in your own words and use a structured brief to guide decisions, browser work, and marketing tasks.

Explore the workflow

Your words give the work its purpose.

A human purpose. A thoughtful next step.FROM INTENT TO ACTION

01 / JEV LLM / HE JEV

JEV LLM starts with a useful brief.

The language layer gives a workflow its context. State the outcome, supply the evidence, and define what a useful answer or action should look like.

JEV LLM organizes the question.

Describe the task, the intended audience, and the constraints together. Specific context makes the desired outcome easier to assess.

JEV LLM supports decision workflows.

Ask for options, trade-offs, and open questions in a consistent format. Review the evidence before using the result.

JEV LLM connects briefs with browser work.

Define which information a browser task should collect and which actions need your review. Carry that intent through each step.

02 / FROM INTENT TO ACTION

JEV LLM gives the task a clear brief.

A JEV LLM brief should state what you want, which evidence matters, and what the result should contain. These examples illustrate the language layer; they are not generated model responses.

AN ILLUSTRATIVE WORKFLOW

Compare these options against our criteria.

THE CONTEXT

The JEV LLM brief names the options, supplies evidence, and states the trade-offs to examine.

THE NEXT STEP

Request a comparison that separates known facts from unanswered questions.

Collect these fields from the allowed pages.

THE CONTEXT

The JEV LLM brief identifies the pages, required fields, and actions that need approval.

THE NEXT STEP

Request a source-linked summary for review before any browser action.

Prepare a campaign brief for this audience.

THE CONTEXT

The JEV LLM brief states the audience, supported claims, tone, and intended channel.

THE NEXT STEP

Request a draft brief; the marketing team approves the final direction.

You review the direction.

A workflow illustration, not a live model response.

DECISIONS BESIDE LANGUAGE MODELS

JEV LLM workflows can separate judgment from writing.

Jev itself does not write text. These third-party resources show how a decision model can classify, route, or guard a workflow while an LLM handles open-ended language.

INTEGRATION GUIDE · LANGCHAIN

Route models and check risky agent tools

LangChain's harness example uses Jev for structured model routing and tool-call risk checks. A generative model remains responsible for the agent's prose and code.

Read the integration guide

THIRD-PARTY VIDEO · SYNTAX

See a smart-home workflow and SDK discussion

Syntax walks through TypeSafe's smart-home example and Vercel integration. The video explains how decisions fit into application code; it is not a live HE Jev service.

Watch the walkthrough

OFFICIAL EVALUATION · AGENT TRACE

Review a support agent's finished run

TypeSafe's agent-trace workflow separates permission checks on irreversible actions from task completion and user satisfaction. Code then routes the trace to closure, issue filing, or human review.

Inspect the agent-trace workflow

COMMUNITY MCP EXAMPLE · JEV MCP

Screen retrieved text for prompt injection

An open-source MCP server shows Jev scoring injection, substance, and relevance before an agent reads a page. Its pass/review/block output is advisory; the calling application enforces policy.

See the MCP example

The name of this page is a search term, not a claim that TypeSafe's Jev is a text-generating LLM. Use a separate language model whenever free-form output is required.

03 / Explore the JEV family

Explore the JEV approaches.

Choose the page that matches your question: applications, model thinking, language, integration, or smaller tasks.