If you’re building anything that has to do with language, such as chatbots, data pipelines, content tools and customer feedback systems, you’ve probably heard the terms natural language processing (NLP) and large language model (LLM) used interchangeably.
Though the two are related, they’re not the same. LLMs are a type of NLP model; think of NLP as the broader field and LLMs as a powerful tool within it.
In this guide, we’ll explore what NLP is, how LLMs fit into the picture and when to use one, the other or both.
What is natural language processing (NLP)?
Natural language processing is a field within artificial intelligence (AI) that focuses on making machines capable of understanding and working with human language. It combines linguistics, computer science and machine learning to help computers analyze, interpret and generate text.
NLP has existed for decades, long before the rise of large language models. While LLMs are the new wave, traditional NLP models are still the backbone of many everyday language processing systems.
Natural language processing is about turning messy, unstructured data into something machines can understand. It powers systems that handle tasks like:
- Sentiment analysis: Figuring out if a review is positive or negative
- Named entity recognition (NER): Identifying people, companies and dates in text
- Part-of-speech tagging: Labeling nouns, verbs and other components in a sentence
- Text classification: Categorizing emails, comments or support tickets
These are classic NLP techniques that don’t require massive models or trillion-token datasets, but they’re incredibly useful for specific language tasks.
Over the years, natural language processing models have evolved. The early days were all about hand-written rules and pattern-matching systems. Then came statistical NLP models, which used labeled training data to improve accuracy. Now, many rely on deep learning methods, which can understand more complex patterns in human language.
Even though they don’t generate paragraphs of text like LLMs do, traditional NLP systems are still widely used. They’re fast, predictable and easy to deploy, especially when resource cost, speed and control are important.
Popular tools for building NLP models include:
- spaCy: Great for fast NER and text pipelines
- NLTK: Good for academic and educational use
- scikit-learn: Often used to train simple machine learning models for text classification or language detection
These tools power many real-world systems, like search engines, email filters, analytics platforms and more.
What are large language models (LLMs)?
Large language models are a newer type of NLP model. They focus on language generation, understanding and reasoning across many types of text-based tasks using large datasets and deep learning.
These models are trained on massive datasets including books, websites, code and social media using deep learning techniques, especially transformers. That architecture gives LLMs the ability to learn context, sentence structure and meaning across huge volumes of text data.
LLMs are built to handle many language tasks at once. You can use the same model to:
- Summarize articles or documents
- Generate blog posts or product descriptions
- Translate between languages
- Answer complex questions
- Write and debug code
- Carry on contextually relevant conversations
This flexibility is why large language models stand out in the NLP space. While traditional NLP systems are often optimized for speed and control, LLMs are designed to generate human-like text that feels fluid, creative and context-aware.
Popular examples include OpenAI’s GPT series, Google’s Gemini, Meta’s LLaMA and Anthropic’s Claude.
While LLMs dominate the spotlight today, they’re still NLP models, just much larger and trained differently. Traditional NLP models still do important work, especially when you need something lightweight, fast or explainable.
Let’s now explore the tradeoffs and differences between them.
Traditional NLP vs. LLM: Key differences and trade-offs
Here’s how LLMs compare to traditional NLP:
| Aspect | Traditional NLP (Natural Language Processing) | LLMs (Large Language Models) |
| Training | Task-specific, often requires labeled data | General-purpose, trained on massive, unlabeled text datasets |
| Model type | Rule-based, statistical or lightweight machine learning models | Deep learning models based on transformer architecture |
| Interpretability | High; each step in the pipeline is traceable | Low; internal reasoning is opaque |
| Resource cost | Low; can run on standard hardware | High; requires powerful GPUs or relies on paid APIs |
| Typical use cases | Sentiment analysis, NER, keyword tagging, POS tagging | Summarization, chatbots, content generation, reasoning tasks |
| Output control | Predictable and consistent | Flexible but can be inconsistent |
| Speed and latency | Fast and efficient | Slower, especially on larger prompts |
| Deployment | Easy to deploy locally or in production | Often requires external APIs or large infrastructure |
For a more detailed breakdown, here is how NLP and LLMs compare in practice:
Training and scale
Traditional NLP models are usually trained for specific tasks, such as detecting spam, classifying sentiment or tagging parts of speech. These models rely on clean, labeled training data and learn only one thing at a time.
LLMs flip this approach. They’re trained once on massive datasets. Instead of learning one task, they learn patterns in how human language works across many language tasks. That’s what lets a single LLM translate text, summarize emails, write SQL queries and generate blog posts, without needing to be retrained for each task.
Interpretability and control
NLP models are easier to understand. You can break them down step-by-step. For example, in a named entity recognition or text classification task, you can trace exactly why the model labeled a word as a person or a company.
Caption: NLP performing named entity recognition
This makes traditional NLP good for use cases that demand explainability, like healthcare or compliance.
LLMs, however, operate more like black boxes. They produce impressive results, but it’s often unclear how or why they arrived at a specific answer. You cannot easily trace the reasoning behind the output and that can be a problem in fields like healthcare, law or finance.
Cost and resources
One of NLP’s big advantages is its efficiency. You can train and run these models on a laptop, a Raspberry Pi or a cheap cloud VM. They’re ideal for use cases where cost, latency and repeatability matter.
LLMs, in contrast, demand significant computational resources. Training requires high-end GPUs and long runtimes. Even inference can be expensive, especially if you’re generating contextually relevant text at scale.
This is why major AI labs raise billions in funding. OpenAI, for example, is now part of a $500 billion infrastructure project called Stargate, backed by SoftBank, Oracle, Microsoft and others. These models need serious hardware and capital to operate.
To visualize just how fast this is scaling, here’s a chart from a 2022 AWS report showing how model sizes, and therefore compute costs, have exploded in recent years:
Caption: Increases in model sizes over time.
Output control
LLMs are built to generate human-like text, which means creativity, but also unpredictability. They’re not fact-checkers. They’re trained to guess what text comes next, not to verify if it’s true. This means they can “hallucinate” information that sounds convincing but is totally made up.
NLP systems, in contrast, return structured, known outputs. You won’t get creativity, but you also won’t get imaginary data.
For example, an NLP model pulling company names from a list of press releases will only return things that are actually there. An LLM summarizing those same releases might invent a fake “strategic partnership with Google” if the prompt is vague or misleading.
When to use NLP and LLMs, and when to combine them
Now that the key differences between NLP and LLMs are clear, the next question is which tool should you use and when.
To answer this question, you need to consider speed, cost, flexibility, accuracy and often, explainability. No model wins by default. It depends on what you’re building, how fast it needs to run and what corners you can’t afford to cut.
When to use NLP
Traditional NLP models are built for structured natural language processing tasks, such as tagging, classifying, extracting or filtering. These systems are tight, predictable and cheap to run at scale.
Take an app that needs to process thousands of support tickets per hour. A machine learning model trained on labeled support data can classify each ticket by intent (billing issue, login problem, feature request). You don’t need a billion-parameter model to do that. You need it to be fast, reliable and traceable.
What matters here is that the output is structured. You know exactly what kind of labels you’re expecting. And because these are rule-based or statistical systems, it’s easy to debug; you can trace the logic all the way back to the source.
When to use LLMs
Use LLMs when the task goes beyond structure and needs flexibility, creativity or a deeper grasp of language. If you’re generating text, holding conversations, translating between languages or working with messy, unpredictable input, that’s where LLMs excel.
Let’s say you’re building a virtual assistant. It doesn’t just need to understand a user’s question. It has to follow context, rephrase answers and sometimes even shift tone or personality. That’s a tall order for classical NLP.
LLMs can handle these tasks because they’ve learned grammar, structure, nuance and how humans actually talk.
This is where companies like Duolingo lean heavily on LLMs, not just for language translation or machine translation, but to teach language conversationally, adapting on the fly to a learner’s skill level. Or take Jasper, which uses LLMs for content creation across marketing workflows, from ad copy to blog intros.
These are tasks where deep neural networks outperform any hand-tuned NLP system because you’re not looking for a fixed label, you’re asking the model to generate human-like text in the tone and context that fits the moment.
So if your task requires nuance, abstraction or multi-step reasoning, LLMs win. They aren’t just responding to rules, they generate responses dynamically based on context, tone and learned language patterns.
When to combine NLP and LLMs
The most practical teams combine the two for performance, cost and quality reasons.
Let’s say you’re working at a company like Zapier, building a system that watches thousands of incoming Slack messages, support emails and feedback forms. You don’t want an LLM processing all of that raw input directly. So first, you run it through NLP:
- Text classification to sort by topic
- Entity recognition to pull out names, companies and dates
- Lightweight semantic analysis to spot potential red flags
Only once that structure is there do you hand it off to the LLM. This hybrid approach gives you speed and control on the front end and the creative flexibility of language generation on the back end.
Same idea if you’re scraping data from messy job boards or public business directories. Use rule-based NLP systems like spaCy or regex pipelines to extract structured fields, like job titles, company names and salary ranges. Then hand those off to an LLM to turn them into clean, readable summaries or to generate personalized outreach messages.
You’re combining rule-based systems for extraction with generative pre-trained transformers for writing.
Wrapping up
LLMs may be stealing the spotlight, but they’re still part of the broader NLP toolbox, not a replacement for it.
Traditional NLP models are fast, cheap and highly effective for structured tasks. LLMs bring power and flexibility when you need open-ended generation, reasoning or conversational flow.
You don’t have to pick one over the other. The smartest teams know when to use each and how to combine them. They use NLP to process, structure and filter, then bring in LLMs for anything that needs creativity, nuance or broader understanding.
So when you’re building language systems, don’t just reach for the flashiest model. Think about what the job actually needs. Sometimes it’s a billion-parameter transformer. Sometimes it’s a simple classifier. And sometimes it’s both, working together.