Generative AI systems, especially those using retrieval-augmented generation (RAG), depend not just on the content of your data, but on how it’s described and organized.
This is where tags become more than just discovery filters; they become semantic handles for intelligent machines.
Just as humans rely on names and categories to reason, so too do AI systems. A tag like pii, financial_forecast, or trusted signals to an Large Language Model (LLM) what a piece of data is, and how it should be used.
In a RAG system:
Tags help control what data is retrieved
Tags shape the prompt context
Tags provide grounding signals to reduce hallucination
Tags help enforce usage policies on the fly
| Tag Category | Why It Matters to AI |
|---|---|
domain |
Helps narrow context to relevant business language |
sensitivity |
Enforces safety controls on what can be retrieved or surfaced |
quality |
Improves factual accuracy by prioritising trusted sources |
data_lifecycle |
Prevents outdated or deprecated information from being used |
use_case |
Aligns retrieval with intent (e.g., forecasting vs. profiling) |
entity_type |
Helps link questions to people, products, locations, etc. |
Imagine a user asks:
"What is the average time to onboard a premium customer?"
Instead of searching blindly across documents, a tagged RAG system can:
Retrieve only datasets tagged with domain: customer, segment: premium, and status: trusted
Exclude deprecated data via product_lifecycle: deprecated
Include SLA guidance from documents tagged policy and use_case: onboarding
The result: a faster, more precise, and governance-compliant response.
If everything is tagged as misc or, worse, not tagged at all - the retrieval layer becomes a liability.
You may:
Surface outdated or unauthorised data
Miss more relevant, certified insights
Make the model appear hallucinated, when in fact it was just underinformed
Just as supervised learning needs labelled training data, enterprise AI needs tagged assets to reason, infer, and answer responsibly.
Tags aren’t just metadata. They’re semantic scaffolding for intelligent systems.
An AI model is only as intelligent as the data it retrieves. Without the right semantic tags, your investment in RAG systems can be undermined by inaccurate, outdated, or non-compliant responses.
We partner with organisations to build the metadata foundation essential for reliable AI. Let us help you assess your data's AI readiness and create a roadmap for success, get in touch!
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