What Is LLM-Based Classification and Why It Beats Traditional NLP for Intent Recognition

What is the key to customer-facing NLP solutions? Indeed, it’s recognizing what the customer wants. That recognition step, classification, maps a free-form message (e.g., “I want to lower my advance payment”) to a clear intent such as Adjust advance payment. Traditionally, teams trained custom NLP models on hundreds of labelled examples per intent. Today, large language models (LLMs) let us do the same job faster and more robustly by describing the task and categories in a prompt, rather than maintaining a time-consuming training pipeline. 

What is Classification?

Single-label classification assigns each user message to exactly one label from a predefined list, your “intent catalog.” Classic NLP achieves this by training a model on many labelled examples. LLM-based approaches achieve it by using prompt instructions: you simply include the intent list with brief descriptions and, optionally, a few examples. Because LLMs already understand language broadly, they can generalize to new phrasings, spelling mistakes, and long or messy sentences without retraining. 

Traditional Intent Classification

1. Collect training and test data: tens or hundreds of example sentences per intent 

2. Train a model on those labelled examples 

3. Evaluate and deploy 

4. Retrain whenever intents change or new variations appear 

   
   

This works, but it strains under reality: large taxonomies (200+ intents), multilingual inputs, overlapping labels, and the constant churn of new phrasing. It’s labor-intensive to label, tune, and retrain; and brittle when data drifts.

The Shift: Classification With LLMs

1. Collect test data: gather 5-10 examples per intent to validate the prompt 

2. Prompt engineering 

3. Evaluate and deploy 

4. Iterate prompt whenever intents change or new variations appear 

   
   

LLMs flip the workflow. Instead of training a dedicated classifier, you describe the task and the allowed categories in a prompt. The model, already pre-trained on vast text data, does the rest: 

• No task-specific training to get started 

• Richer semantic understanding → fewer “no match” outcomes 

• Robust to typos and long sentences 

• Fast iteration → change the taxonomy or examples in the prompt, no extensive retraining loop 

Side-by-Side: Traditional vs. LLM Classification 

Dimension	Traditional NLP Classifier	LLM-Based Classifier
Startup effort	High: large amount of labelled data per intent	Low: only labelled data for validation set + intent description in prompt
Maintenance	Retrain for new intents/variants	Edit prompt/taxonomy; no retrain
Robustness	Sensitive to typos/long phrasing	Naturally robust, better semantics
Overlapping intents	Hard to manage: retrain on more data, or use entities	Better disambiguation via instructions
Time-to-value	Weeks to months	Hours to days
Total cost of ownership	High (labelling + retraining)	Lower (prompt ops + evaluation)

Teams moving from a traditional classifier to an LLM (on mixed, multilingual customer text) typically see: 

• Higher recognition, especially on tricky or novel phrasing 

• Fewer no-matches thanks to the general knowledge of LLMs 

• Shorter iteration cycles when intents or wording change (no retraining) 

How to Get Started (A Practical Blueprint) 

1. List your intents with crisp, non-overlapping descriptions and 3–5 real-life examples each. 

2. Write the prompt to enforce rules: choose exactly one, never invent categories, follow language constraints. 

3. Evaluate on a held-out test set per language; track accuracy, no-match rate, and confusion pairs. 

4. Tighten the taxonomy where overlaps cause confusion (rename, merge/split, add examples). 

5. Deploy behind your chatbot’s router. Start with classification only; keep answer generation separate at first. 

6. Iterate quickly: adjust descriptions and examples based on real errors. 

   
   

As all processes this one has some caveats to consider: guard against prompt drift by keeping intent descriptions tight and mutually exclusive as your catalog grows; manage latency and cost by choosing an affordable, fast model tier for classification and caching where possible; uphold evaluation discipline by maintaining a living test set and rechecking after any taxonomy edits. An easy way to tackle the LLM classification process without succumbing to the classic pitfalls is by making use of tooling. The Reimagine developed tool Prowli is a low-code platform designed uniquely to built and manage LLM classification use cases.  

Interested in a demo of Prowli? Contact toon.lybaert@reimagine.be.

The Bottom Line 

Traditional intent classifiers are showing their age for high-variance, multi-language customer messages. LLM-based classification is easier to set up, faster to iterate, and more straightforward to maintain, and it plugs neatly into today’s chatbot stacks. That’s why more teams are transitioning to GenAI-driven routing now, while keeping answer generation on a pragmatic, governed path. 

FAQs 

1. Do I still need training data? You need evaluation data to know whether your prompt and taxonomy work. Start small; grow over time. 

2. What about answer generation? Treat it separately. Many teams run LLM classification to choose the right flow, then answer via scripted logic (or RAG) for control and compliance. You can mix and match as needs evolve. 

3. Isn’t this just “prompt engineering”? Yes! And that’s the point. You move complexity from model training to taxonomy + prompt design, which is easier to change and audit. 

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