Eachlabs Decisions.

each::labs Decisions Overview

Ask typed questions about any content and get calibrated answers: yes/no probabilities, choices and scores.

Typed questions request yes/no probabilities, choices or scores against supplied content.

each::labs Decisions is available through each::labs in the each-tools family. Required fields are model, state, questions.

The operation uses the supplied source to perform its specific task. It can be placed between source preparation and final delivery without combining unrelated changes into the same request.

Use the each::labs Decisions API when you need this transformation or analysis as a repeatable step in an application or creative pipeline. The sections below distinguish the required material, adjustable controls and checks that matter before delivery.

Capabilities

• Typed questions request yes/no probabilities, choices or scores against supplied content.
• Produces a object result for the next stage of your workflow.
• Runs the requested operation on the supplied input rather than requiring a manual editing session.
• Can be used as a focused processing step in an each::labs application.
• Keeps the original source separate from the returned processing result.

Use Cases for each::labs Decisions

each::labs Decisions is useful when this specific operation fits between source preparation and delivery. These scenarios show how it can support different parts of a working pipeline.

Creators

Score whether a creative matches a brief. Use a representative source or a concise brief, then inspect the result before preparing a larger set of assets.

Design and marketing teams

Select a category for supplied content. Compare the result with the intended message, composition and destination. Keep reviewable versions of the source and output.

Developers

Add a typed quality decision to a workflow. Inspect the actual returned value before connecting it to another step, and keep the chosen request settings with the result.

Start with a clear definition of the desired result and use the exposed controls to reach it. Keep any additional generation, editing or application logic in separate steps so that the effect of this operation remains easy to review.

Tips and Tricks

Write each question so that its requested type, instructions and criteria agree. Use concrete categories or a clearly defined score scale, then evaluate representative examples before placing decisions into a larger workflow.

Choose a representative source for the first pass. Keep the original material and record the request values so that a successful result can be reproduced as part of your wider editing or processing sequence.

• Example direction: “Does this supplied creative match the product category stated in the brief?”
• Example direction: “Score how clearly the supplied description states its main subject.”

Compare one deliberate adjustment at a time. Inspect the part of the result affected by that change, then check the rest of the asset or response for unwanted side effects before reusing the settings.

Technical Specifications

• Result type: object.
• Required fields: model, state, questions.
• model: string.
• questions: object.
• state: string.

Values must use the types and accepted options listed in the parameter reference. A displayed default does not remove a required-field requirement. Choose a supported output setting for this entry rather than borrowing a preset from a related model.

Things to Be Aware Of

Review the returned probability, choice or score against the supplied content and stated criteria. A numerical result is not a guarantee of factual correctness or consistent behavior on different inputs.

Optional settings are not necessarily interchangeable with those on another entry in the same family. Use the accepted names, types and values shown for this operation.

Check the returned content against the original input and your application requirements. A successful request means the operation completed; your application still needs to verify that the response is suitable for the next step.

Key Considerations

Choose a decision model from the documented model-list endpoint; a calibrated probability or score is still a model estimate.

Usage-based: the charge is the cost recorded in output.usage.cost for the selected model and request. The catalog defines no universal fixed per-request rate for this entry.

Prepare model, state, questions before starting. Keep these settings aligned with the intended delivery, especially when a later step expects a particular type or format.

Run a representative request before scaling to a larger collection. Judge the result against the source and the brief, and account for all generated outputs or usage-sensitive options when estimating a batch.

Limitations

This entry supports the operation and controls described here; it does not provide a general interface for unrelated transformations.

Unsupported parameters do not establish additional capabilities. A generated result can differ from the intended brief, so review detail and consistency before publication.