> ## Documentation Index > Fetch the complete documentation index at: https://docs.markifact.com/llms.txt > Use this file to discover all available pages before exploring further. # Jev Evaluate > Use TypeSafe Jev for fast, low-credit classification, yes/no checks, and scoring across thousands of text items Use TypeSafe Jev for fast, low-credit classification, yes/no checks, and scoring across thousands of text items. Evaluate content as a whole or each list item with automatic batching. Text only; images are not supported. | | | | -------------------- | ----------------- | | **App** | AI | | **Operation ID** | `ai_jev_evaluate` | | **Type** | Action | | **Connection** | None | | **Credits per run** | 1 | | **Agent / MCP tool** | Yes | ## Inputs | Field | Type | Required | Default | Description | | ----------------- | ----------------------------------- | -------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `processing_mode` | enum (`whole_content`, `each_item`) | No | `whole_content` | whole\_content (default): evaluate all content together, including a conversation or related records. each\_item: apply the same questions independently to each list item, automatically batching many evaluations per API request. | | `content` | string or object or array of any | Yes | - | Text, a JSON object, or a list to evaluate. Accepts native objects/lists or JSON text. In each\_item mode provide a list, e.g. \[\{"query":"buy running shoes","clicks":120}]. Content is supplied as text/structured data; media URLs are not fetched. | | `content_fields` | array of string | No | - | In each\_item mode, top-level keys to evaluate from each object, e.g. \['query']. Omit or pass \[] to evaluate the entire item. Other fields are preserved in the output. Selected keys must exist in every item. | | `questions` | object or string | Yes | - | Nonempty map of questions keyed by output name, or its JSON string. Use the exact types choice, score, and noul; yes/no uses noul, not boolean or yes\_no. Each question needs instructions describing the task; its output name is only an ID. You can combine different question types in one map. Questions run independently. Classification (choice): choose one category. criteria is an object mapping category names to descriptions (or null). Include an other/no-match category when appropriate. Example: \{"intent":\{"type":"choice","instructions":"Classify the search intent.","criteria":\{"informational":"Users seeking information or answers","transactional":"Users ready to make a purchase or complete an action"}}}. Returns choice, confidence, and probabilities for the categories. Scoring (score): rate one dimension using criteria as an ordered array of descriptive levels, lowest to highest. Use 2 to 10 concrete descriptions, not numeric labels. Example: \{"offer\_clarity":\{"type":"score","instructions":"How clearly does the ad describe its offer?","criteria":\["No product or service is identified","The product or service is named but its benefit is unclear","The product or service and its customer benefit are explicit"]}}. Returns score, confidence, probabilities, and legend. Levels start at 0; this example returns a score from 0 to 2, including fractional values. Yes/no (noul): ask whether a condition holds. criteria is optional; when supplied, it is an object with true and false descriptions. Example: \{"has\_cta":\{"type":"noul","instructions":"Does the ad explicitly ask the reader to take an action?","criteria":\{"true":"Explicitly asks the reader to buy, sign up, book, call, or visit","false":"No explicit request to take an action"}}}. Returns noul, the probability of yes from 0 to 1, not a boolean or a separate confidence. Use several noul questions when multiple labels can apply. In each\_item mode define questions once, referring to the current item or its field names; Markifact binds each question to its item. No \{item} placeholder is needed. Instructions and criteria descriptions also accept structured objects/arrays; these pass through without losing nested fields. | | `flatten_output` | boolean | No | `False` | Default false: return native answer objects keyed by question ID; list results are an array of \{item, answers}. True: add flat fields named after each question, with \_confidence/\_probabilities for choice and score, \_legend for score, and \_probability for noul. A flattened noul is true at probability >= 0.5; use its probability for your own threshold. Existing columns are never overwritten. | | `model` | string | No | `jev-latest` | Jev model ID or alias. Defaults to jev-latest, which follows the latest stable release. | ## Output **Type**: `Any` Whole content returns answers directly keyed by question ID; each\_item returns an array \[\{item, answers}] in input order, with no rows wrapper. Model and token usage are kept in execution logs, not output. Native answers preserve choice/noul/score, probabilities, confidence and score legends. flatten\_output=true places answers in named columns (original columns retained for list items): \, \\_confidence, \\_probabilities, \\_legend for scores, or \ boolean and \\_probability for noul. Flattened booleans use probability >= 0.5. No text explanations are generated. **Fields**: dynamic (depend on the inputs) **Example**: ```json theme={"dark"} { "intent": { "type": "choice", "choice": "transactional", "confidence": 1.0, "probabilities": { "informational": 0.0, "transactional": 1.0 } } } ```