Assess my taxonomy

Assess my taxonomy is a tool designed to refine and optimize taxonomies used for categorizing text responses, ensuring they are clear, concise, and effectively structured.


The tool 'Assess my taxonomy' operates on a low-code AI workflow platform, integrating a language model, likely GPT-4, to assist users in enhancing their taxonomy or coding framework. It is particularly useful for organizing open-ended survey responses or reviews. Users can choose to evaluate their entire taxonomy list or a specific response and its associated code. The tool then processes this information, applying the language model's capabilities to suggest improvements, merge similar codes, and eliminate redundancies, thereby streamlining the taxonomy for better usability and analysis.

Use cases

Use cases for 'Assess my taxonomy' include academic researchers categorizing qualitative data, market researchers analyzing open-ended survey responses, customer service teams organizing feedback, and content managers structuring large volumes of user-generated content. The tool's ability to refine taxonomies makes it valuable for any scenario where structured categorization of text is essential.


The benefits of using 'Assess my taxonomy' include a more efficient and effective categorization process, reduced ambiguity in code assignment, and enhanced data analysis capabilities. By refining taxonomies, users can achieve greater accuracy in interpreting text responses, leading to more informed decision-making and insights.

How it works

Upon selection of the desired assessment type, users input their taxonomy or a single code and corresponding text. The tool leverages the language model to analyze the input against set criteria, such as code length and content relevance. For taxonomy lists, it suggests enhancements and restructures the codes for optimal clarity and relevance. For individual code assessments, it ensures the code accurately reflects the content of the response. A JavaScript transformation then tailors the output to the user's initial selection, providing an improved taxonomy or a revised code with a descriptive rationale.

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