FEATURE -Generate Contextual Examples for User Input Based on Vocabulary and Mobile-First Design
As a system, when a user provides a spoken word, typed word, or a word from the document, in the search control, I should generate a list of examples of the word in context, taking into account the user's existing vocabulary or previously understood words, mobile-first design, and handling variations in spelling and capitalization, so that:
- The examples are ranked by a relevancy algorithm that considers their relation to an optional given document and the user's understood vocabulary - or alternatively, relevancy standing with Chat GPT or generic algorithm for relevancy
- The user can understand the proper usage of the word based on contextually relevant examples that are tailored to their language proficiency.
- The user can explore different ways the word is used in various situations, especially those related to the optional given document.
- The system logs user interactions with example sentences and any additional features provided to assess their understanding and learning progress.
- The example sentences are displayed in a clear and organized format, optimized for mobile devices, with each sentence listed on a separate line.
- The system must accurately identify and highlight the given word or phrase in each sentence, taking into account variations in spelling, capitalization, and distinguishing them from grammatical errors.
- if entered the word with the error the system should provide the properly spelled alternatives
- if spoken, the system should provide the alternative words in speech- to -text
- The system must display a list of at least five sentences that contain the given word or phrase, indicating context variations in the output.
- system should provide the functions that should be used to find the meaning in the decreasing order in terms of concepts - simplify, dictionary, translate, and navigational controls in the detailed view.