Generative AI (GenAI) and Large Language Models (LLMs) have provided numerous solutions in many applications. The original public application of question-answering, popularized by ChatGPT, has become ubiquitous and has changed the way people search for information on the web and more recently inside the enterprise as well. Instead of keyword searches, people are submitting full sentence questions that can be refined. Full sentence queries allow for complex questions beyond just retrieving information “on a subject.” The system’s chat-type responses with suggestions on how to refine the query are especially useful and have improved the user experience of conducting searches and getting results.
The question has arisen: “Why do you still need taxonomies and semantic tagging when AI could do all of that automatically?” Although GenAI has improved the experience in getting answers to questions, the accuracy and consistency of the results can be lacking. As taxonomies have enhanced traditional enterprise search results, taxonomies can also improve GenAI query results.
The Value of Taxonomies in Search and Findabililty
In the digital space, search engines at first seemed to compete with taxonomies, but soon it became apparent that search alone had short-comings. The same term with multiple meanings or the negation of a term results in false search results. On the other hand, the existence of synonyms for the same concept results in not retrieving (missing) desired results that are described with a different synonymous term. Furthermore, the search box by itself does not allow users to refine or expand their search results.
Taxonomy concepts and semantic tagging for what content is about, not merely the mention of text strings, achieve better search results. Taxonomies bring together different synonyms or names of the same thing. Additionally, the display or partial display of taxonomies, as browsable hierarchies, filtering facets, or term matches to search strings in drop down (sometimes type-ahead) lists, have given users more control over search and more confidence in the results. Hierarchy can also be utilized in information retrieval, whereby a concept retrieves not only the content it has been tagged to but also the content that has been tagged to each of its narrower concepts.
The Value of Taxonomies in Supporting Generative AI and LLMs
Like search, GenAI can be implemented without taxonomies, but combined with taxonomies for an enterprise implementation yields better results. LLMs work with patterns and predictions, and they do not always resolve synonyms. If you query “What are the leading U.S. pharmaceutical companies?” and “What are the leading U.S. drug companies?,” you don’t get identical results, although the answers are similar.
When querying internal, enterprise information or data, a higher level of accuracy is expected and needed, and synonyms need to be made explicit. This can be done through a taxonomy, which the LLMs can reference when Retrieval Augmented Generation (RAG) is implemented, which reduces hallucinations, contradictions, and inconsistencies.
Other taxonomy features than the synonyms may also be leveraged with RAG. Relationships between concepts (broader, narrower, and related) in the taxonomy can extend the retrieval. The hierarchy feature of a taxonomy also serves LLMs by providing context and thus more specific meaning for concepts through their hierarchical relationships. Furthermore, there may be terminology used uniquely to the enterprise, which an LLM wouldn’t know, such as “active customer.” Adding definitions to taxonomy concepts is useful both to the LLMs and to the human users.
Data-heavy organizations that implement LLMs internally combined with a custom taxonomy at the enterprise level usually opt to go a step further and implement GraphRAG. GraphRAG combines LLMs with a knowledge graph, which comprises a taxonomy, ontology, and linked instance data in a graph database. This way, the LLMs can make use of the explicit semantic relationships in the knowledge graph, which support complex, multi-component queries. Because of their support for GenAI and LLMs using RAG or GraphRAG, taxonomies and semantic tagging are more relevant than ever.
Using Generative AI to Create Taxonomies
The next obvious question is “Can you use GenAI and LLMs to generate taxonomies?” Yes, you can. Taxonomies, however, are more complicated and nuanced than they might seem. Taxonomies should be customized to the content and data they will be used for, the end users’ needs and expectations, the use cases or purposes they will serve, and front-end application requirements. You would need to provide very detailed and lengthy prompts just to get started.
The best approach is to use GenAI for a taxonomy selectively. You may generate selected parts or branches of a taxonomy (such as topics, trends, technologies, or regulatory framework), but not for an organization’s own products, services, departments, or offices.
GenAI is also suitable for various sub-tasks of taxonomy creation, such organizing a flat list of terms into a suggested hierarchy, suggesting alternative labels (synonyms) for a concept, suggesting narrower concepts for a concept, generating definitions for concepts, or explaining the relationship of two technical concepts to each other (broader/narrower inclusive, related and overlapping, or synonymous).
You can also use GenAI to generate a suggested starter taxonomy to use as a source for ideas and inspiration without adopting most of it. In any case, the specialized role of custom taxonomy should always involve human-the-loop interaction, instructions, review, and editing.
Taxonomy management software vendors are beginning to incorporate LLMs into their products to assist with the auto-generation of taxonomies or parts of taxonomies that their software manages. The vendor with the most advanced feature is Graphwise. I’ve had the opportunity to try out the Taxonomy Builder feature, which is integrated into Graphwise’s Graph Modeling taxonomy/ontology management tool. You can read more about it in “How AI and Taxonomy Builder Support the Building of Taxonomies.”
Using generative AI to assist in the creation of custom taxonomies accelerates the process and supports taxonomy best practices with which project owners or subject matter experts may not be familiar. It also helps skilled taxonomists create taxonomies in subject domains in which they lack expert knowledge.
The Role of Taxonomists with AI
AI has led to the decline in certain information management jobs but not others. The role of professional content indexers has definitely declined with AI (not even GenAI) over the past decades. I know, as I used to be an indexer. Human tagging as a task, not a job role, continues to a limited degree, but increasingly the task involves reviewing and accepting/rejecting automated tagging suggestions.
The role of taxonomists will probably not decline, but will change. The need for taxonomies is growing. With GenAI, professional taxonomists are able to create taxonomies faster, so the cost of taxonomy creation is going down. (The LLM subscriptions are already being paid for other enterprise uses.) The use of GenAI to help create taxonomies also make their creation more feasible for those who are not taxonomists. Experienced taxonomists are still needed to provide initial guidance and ideally review and feedback.
The role of taxonomy consultants, as myself, will likely also change. Instead of taxonomy project consulting engagements that last many months with intensive information gathering and numerous stakeholder interviews, followed by manual taxonomy creation with iterative reviews, more consulting engagements will involve helping design the start of the taxonomy, guiding clients to use AI, providing feedback, and developing the taxonomy governance plan.
Taxonomists are identifying more ways to utilize GenAI in their work. I will write another blog post on that in the future, and I will be chairing a panel of taxonomists using GenAI at the next Taxonomy Boot Camp conference in Washington, DC, November 16-17, 2026.