Abstract: In examples, an execution chain used to process includes one or more blocks, where each block includes at least one of a machine learning (ML) definition and/or a set of programmatic operations. As an example, an ML definition of an ML block includes a prompt to be processed by an ML model. As another example, the ML definition includes a prompt template, which may be populated based on a previous block of the execution chain. Further, a programmatic block of the execution chain can include any of a variety of operations, for example to obtain data from a data source and/or to prompt a user for input, thereby obtaining additional data that may be used to ground an ML model for a subsequent machine learning block of the execution chain.

Abstract: A user query may be evaluated to provide a result set. In case the results do not reflect the user's intent, the device may provide recourse options for adjusting the query in a manner that yields more desirable results, e.g., a suggestion at the top of the result set for a different spelling, or recommendations at the end of the results set for additional query techniques that may yield more accurate results. However, such presentation of recourse options may clutter the user interface and/or go unnoticed by the user. Instead, an adjusted query may be identified with an interpreted probability of reflecting the intent of the query. An adjustment option describing the adjusted query may be inserted into the result set, between a higher-probability first result and a lower-probability second result. Selection of the adjustment option may cause the adjusted query to be evaluated on behalf of the user.

Abstract: A user query may be evaluated to provide a result set. In case the results do not reflect the user's intent, the device may provide recourse options for adjusting the query in a manner that yields more desirable results, e.g., a suggestion at the top of the result set for a different spelling, or recommendations at the end of the results set for additional query techniques that may yield more accurate results. However, such presentation of recourse options may clutter the user interface and/or go unnoticed by the user. Instead, an adjusted query may be identified with an interpreted probability of reflecting the intent of the query. An adjustment option describing the adjusted query may be inserted into the result set, between a higher-probability first result and a lower-probability second result. Selection of the adjustment option may cause the adjusted query to be evaluated on behalf of the user.

Abstract: One or more techniques and/or systems are provided for constructing a content interface layout. For example a search query may be received from a user of a device. A content interest context for the user may be identified (e.g., a time of day, a season, a user location, a user social network post about planting a garden, device type of the user, etc.). Content interfaces (e.g., a gardening image collection, a tomato planting instructional app, a soil related topic search suggestion, etc.) may be populated within content interface slots of a search results canvas for the search query based upon user engagement metrics and the content interest context. In this way, content interfaces, comprising supplemental content, may be assigned to content interface slots so that such content interfaces have a high probability of being relevant to and/or engaged by the user.