By Marc Stephenson |

January 24, 2025

How to achieve more accurate search results with AI auto-tagging

For many years, organisations (and in particular users) have sought to avoid adding metadata to content. I get it. It’s boring, it’s time-consuming, and it’s difficult – a killer combination that almost guarantees failure.

People say: “You don’t need to add metadata anymore – we have good search engines.

People have been saying this for years, because technology vendors and search engine providers have been saying it for years. But marketing isn’t always the truth.

Effectively, yes, search engines are pretty good these days – and have been for a while. However, for organisational search, are they good enough? If you want to find a decent HDTV, search is OK because you don’t mind getting 4 million hits. But if you want to find the official company maternity policy, you only want one hit. And you want it to be the right one. First time.

AI auto-tagging metadata

AI tools, such as ChatGPT, are becoming the new search engines, and in theory, they can return useful search results.

But there is an issue – these AI tools work on the principle of most likely results, NOT the most accurate results (and how can they, as they have no concept of “accuracy”). But there is a solution. Meet Artificial Intelligence (AI) for metadata tagging – or AI auto-tagging for short.

How AI auto-tagging can help your organisation

Using Artificial Intelligence for auto-tagging and then using search for retrieval, is a great way to improve search results.

The retrieval will now be accurate, as the (auto-)tagging is. You have replaced the human-driven, boring, time-consuming, difficult tagging task with AI – to me that sounds exactly like what AI should be doing!

Generic context vs specific context

You can also take another step to improving search accuracy if the auto-tagging is given direction by the specific context of what is to be tagged.

What is AI auto-tagging?
  • Artificial Intelligence auto-tagging is the process of using AI to automatically assign descriptive metadata to large amounts of files.
  • AI auto-tagging uses artificial intelligence to analyse the content of unstructured data, generate a relevant term, description, tag, keyword or asset-specific information that describes it – and then assign these metadata tags to your files, assets and documents.
  • This quickly makes your content searchable by key terms – not only negating the need to invest vast amounts of time and resource to manually populate thousands of files with metadata – but also to rapidly deliver reliable and accurate search results.

AI tools, and Large Language Models (LLMs) specifically, are very good at using the generic context – LLMs are gigantic gatherers of generic context. The key is to augment this generic context (common sense if you like), with the domain specifics of what you are tagging. This has implementation implications, such as needing a localised LLM instance, in order to protect your IP, but the best way to use the specifics of your content is to use a taxonomy or ontology.

You can, of course, also use AI to generate a taxonomy or ontology, but that’s a subject for another blog.

In short, AI can be a valuable tool in information management, but it needs human expertise to really leverage that value.

Talk to us about AI auto-tagging today.