2025-09-24

NLP vs. LLMs: What's the Difference—and How Are They Connected?

What exactly is the difference between NLP and LLMs? Are they interchangeable? And when should you use one over the other? Let’s break it down.

Reading time: 5 minutes

By Daniel Gleim

Technical Content Creator

NLP vs. LLMs: What’s the Difference—and How Are They Connected?

Natural Language Processing (NLP) and Large Language Models (LLMs) are both key players in the world of artificial intelligence. They power the tools we use every day—chatbots, document summarizers, translation services, and much more.

But what exactly is the difference between NLP and LLMs? Are they interchangeable? And when should you use one over the other?

Let’s break it down.

What is NLP?

Natural Language Processing (NLP) is a field of artificial intelligence focused on helping computers understand, interpret, and generate human language. It combines elements of linguistics, computer science, and machine learning to enable machines to make sense of unstructured text.

NLP has been around for decades and includes a wide range of tasks, such as:

Sentiment analysis
Named entity recognition (NER)
Text classification
Part-of-speech tagging
Language translation

NLP systems can be built using various approaches—from rule-based methods and statistical models to modern deep learning architectures.

What are LLMs?

Large Language Models (LLMs) are deep learning models trained on massive datasets to understand and generate human-like text. They are typically based on a neural network architecture called the Transformer, which was introduced by Google in 2017.

Transformers revolutionized NLP by enabling models to understand the context of a word based on its relationship to all other words in a sentence, rather than processing text word-by-word in sequence. This mechanism, called self-attention, allows for more nuanced and accurate language understanding.

Some well-known LLMs include:

GPT (by OpenAI)
BERT (by Google)
LLaMA, Claude, and others

LLMs are capable of performing a wide variety of tasks—translation, summarization, answering questions, generating content—with little to no task-specific training.

Key Differences Between NLP and LLMs

It’s easy to blur the lines between NLP and LLMs, but they aren’t the same thing.

The key distinction:

NLP is the broader field that defines the problems and methods,
LLMs are one of the latest, most powerful tools within that field.

Traditional NLP methods can be lightweight, modular, and easier to interpret. They’re often used when tasks are well-defined, data is limited, or the need for speed and control is high. For instance, a simple rule-based or statistical model might be enough to classify emails or extract keywords.

LLMs, in contrast, are large, complex, and require significant compute resources—but they offer remarkable flexibility and fluency across a wide range of tasks. With minimal prompting, they can handle translation, summarization, Q&A, and much more—all with human-like output.

A Helpful Analogy

Think of NLP as a toolbox, and LLMs as one of the newest power tools inside it. Traditional tools still work well for many jobs, but when you need something more powerful or adaptable, LLMs are often the better choice.

How NLP and LLMs Are Connected

LLMs don’t replace NLP—they extend it.

Everything an LLM does falls under the umbrella of NLP. The difference lies in how the task is accomplished. Where traditional NLP often required separate models or rule-based systems for each individual task, LLMs can handle many of them with a single, pre-trained model and a well-crafted prompt.

For example, tasks like summarization or translation once required building and training dedicated systems. Now, those same tasks can often be handled by prompting an LLM with a few lines of instruction—no extra training required.

In short:

NLP defines the goals (e.g., understand, analyze, or generate language)
LLMs are a modern way to achieve those goals

Real-World Use Cases

Understanding where NLP and LLMs show up in real applications makes the distinction even clearer.

Traditional NLP Use Cases

Spam filtering using keyword or statistical models
Basic sentiment analysis with traditional classifiers
Named entity recognition in structured text
Text preprocessing in data pipelines

These systems are often lightweight, fast, and easy to maintain—ideal for narrow, well-scoped problems.

LLM Use Cases

Chatbots and virtual assistants that understand and generate natural responses
Summarization tools that digest entire articles into key takeaways
Q&A systems that can read and respond to complex documents
Multilingual translation with context-sensitive fluency
Creative content generation, from emails to blog drafts to code

LLMs are especially valuable when the task is open-ended, multi-step, or lacks clear structure.

Advantages and Disadvantages

Understanding the strengths and trade-offs of both traditional NLP and LLMs can help you decide which is better suited for your project.

Traditional NLP

Advantages

Lightweight and fast—even on low-end machines
Easier to interpret and explain
Cost-effective and resource-efficient
Mature ecosystem of libraries and tools

Disadvantages

Limited flexibility for complex tasks
Lower performance on nuanced language understanding
Requires more manual setup and customization

Large Language Models (LLMs)

Advantages

Highly flexible—can perform many tasks with one model
Fluent and natural language generation
Strong results with little or no task-specific training
Great for handling ambiguity and context

Disadvantages

Computationally expensive and resource-heavy
Black box behavior—hard to understand how decisions are made
Can produce inaccurate or unpredictable output
Cost of deployment and maintenance can be high

When to Use What

Choosing between traditional NLP and LLMs depends on your specific needs.

Use Traditional NLP when:

The task is simple, structured, or rules-based
You have limited computational resources
Speed, interpretability, or small-scale deployment is a priority

Use LLMs when:

The task is complex, creative, or open-ended
You want a general-purpose model that handles many tasks
You don’t have labeled training data but need strong results quickly
You value natural-sounding, flexible language output

In many modern systems, the two approaches are even combined. For example, you might use traditional NLP for extracting structured data from a document, then feed that data into an LLM to generate a summary or perform deeper analysis.

Summary

Natural Language Processing and Large Language Models go hand in hand. NLP is the foundation that defines the problems and methods for working with human language. LLMs are one of the most powerful tools developed to solve those problems—capable, flexible, and increasingly widespread.

Understanding the difference helps you choose the right approach, whether you’re building a lightweight rule-based system or harnessing the power of state-of-the-art language models.

In the end, it’s not about choosing sides.
It’s about using the right tool for the job.