NLP vs LLM – What’s the Difference?

• Michael Willson
• March 20, 2025

We all talk to Siri and Alexa every day, asking questions, seeking advice, or even just for fun. But have you ever paused and wondered how exactly these machines understand us? Terms like “NLP” and “LLM” get thrown around a lot, yet their real meanings often remain unclear. So, what’s the actual difference between NLP and LLM, and does knowing it really matter? Keep reading to find out. 

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is an area within artificial intelligence. It helps machines read, interpret, and create human language. NLP involves several tasks and uses multiple methods like rules and machine learning.

What tasks does NLP include?

NLP covers these common tasks:

• Text Classification: Sorting text into groups, such as detecting spam emails.
• Sentiment Analysis: Checking if text expresses a positive or negative feeling, like online reviews.
• Machine Translation: Changing text from one language to another, such as Google Translate.
• Named Entity Recognition (NER): Finding names, places, and organizations within text.
• Speech Recognition: Turning spoken words into text, used by virtual assistants like Siri or Alexa.

NLP uses various methods, from basic rule-based approaches to advanced statistical and machine learning techniques. Understanding NLP and large language models is easier with the Certified Artificial Intelligence (AI) Expert™ certification.

What are Large Language Models (LLMs)?

Large Language Models (LLMs) are a specific type of NLP technology. They’re built using deep learning, a kind of advanced AI. These models learn from enormous amounts of text data to generate natural-sounding human language.

What makes LLMs special?

LLMs stand out because of:

• Huge Scale: They have billions of parameters, helping them learn complex language patterns.
• Two-step Training: First, they learn general language patterns. Then they fine-tune for specific tasks.
• Versatility: They can handle multiple tasks without needing separate programming.
• Contextual Responses: They produce text that feels natural and relevant.

Famous examples of LLMs are OpenAI’s GPT, Google’s BERT, and Meta’s LLaMA.

NLP vs LLM: What are the main differences?

Let’s break down the differences between NLP and LLM clearly:

Scope and Purpose

• NLP: Includes many language tasks using various methods (rules, statistics, machine learning).
• LLMs: Focus specifically on generating human-like text using deep learning.

Model Size and Complexity

• NLP: Can range from small, simple models to advanced ones, usually smaller than LLMs.
• LLMs: Huge, complex models with billions of parameters to deeply understand language.

Training Data and Methods

• NLP: Usually trained on specific datasets related to the task.
• LLMs: Train on massive, varied data, learning general language skills applicable to multiple tasks.

Adaptability and Performance

• NLP: Performs well on specific tasks, but adapting to new tasks can require significant changes.
• LLMs: Easily adapt to different tasks without major modifications.

Resource Requirements

• NLP: Requires less computational power, making it easier to use on smaller devices.
• LLMs: Need substantial computing resources, including specialized hardware.

Recent developments in NLP and LLMs (2025)

What’s new in NLP?

• Bias Detection: New tools identify and reduce bias in NLP models, helping ensure fairness.
• Regulatory Standards: Governments introduced stricter rules to make AI models safer and more reliable.

What’s new in LLMs?

• Multimodal Capabilities: Now LLMs handle more than text. They process images and other data types, enhancing user interactions.
• Integration with Robotics: Models like Google’s Gemini Robotics connect language understanding with robots performing physical tasks.
• Better Global Language Support: Partnerships between companies like Orange, Meta, and OpenAI enhance support for regional languages.

Practical applications of NLP and LLMs

Where is NLP used today?

• Customer Support: Chatbots quickly answer customer questions.
• Content Moderation: NLP helps automatically remove harmful content on social media.
• Healthcare: Doctors use NLP to quickly review patient records. Amazon Comprehend Medical, for example, helps interpret medical data.
• Legal Tech: Law firms use NLP to review documents quickly, like Lex Machina identifying key points in legal documents.

Where do we see LLMs in action?

• Content Creation: Businesses use LLMs (like GPT-4) for quickly creating articles, emails, or marketing content.
• Programming Assistance: Tools like GitHub Copilot suggest code snippets to help programmers work faster.
• Educational Tools: Platforms like Khanmigo use LLMs to provide personalized tutoring for students.

Challenges faced by NLP and LLMs

Challenges for NLP

• Context Issues: NLP sometimes struggles to interpret unclear meanings or sarcasm correctly.
• Bias Problems: NLP tools may replicate societal biases from training data, which requires careful monitoring.

Challenges for LLMs

• High Resource Usage: LLMs need significant computational power, making them less accessible for small businesses.
• Accuracy and Hallucinations: LLMs sometimes produce inaccurate information. Fact-checking is always necessary.

NLP vs LLM: Real-world Examples 

Let’s understand differences between NLP and LLM clearly with real examples from 2025:

NLP Examples

• Healthcare Records: Hospitals use NLP to quickly extract important information from medical records for faster treatment.
• Voice Assistants: Alexa listens and acts on commands thanks to NLP technology.

LLM Examples

• Vacation Planning: Tools like Gemini 2.0 Flash plan detailed trips based on user instructions.
• Article Outlines: DeepSeek R1 creates structured outlines for articles, improving writers’ productivity.

NLP vs LLM: What’s trending in 2025?

Trending NLP advancements

• Multilingual Improvements: NLP is expanding to support more languages, especially in regions previously overlooked.
• Emotion Recognition: New NLP research explores detecting emotions in written text, improving interactions.

Trending LLM advancements

• Physical World Integration: LLMs like Google’s Gemini Robotics combine language skills with physical actions in robots.
• Efficient, Smaller Models: Researchers are finding ways to reduce LLM size, making them easier to use on mobile devices.

Should you pick NLP or LLM?

Deciding between NLP and LLM depends on your needs:

• Choose NLP: For structured tasks, precise data analysis, or clearly defined applications.
• Choose LLM: For creative tasks, flexible responses, coding help, or general-purpose conversational tools.

In practice, many businesses combine both technologies. Stay ahead in AI with our Unlimited Learning Subscription (AI)—learn at your own pace.

What are the future trends for NLP and LLM?

Future NLP Trends

• Improved Multilingual Support: Ongoing efforts will further enhance NLP for less common languages, especially in Africa and Asia.
• Advanced Emotion Detection: NLP will get better at recognizing emotions, significantly improving customer service interactions.

Future LLM Trends

• More Robotics Integration: Expect more language models integrated directly into robots, improving automation.
• Compact and Efficient Models: Smaller, more efficient LLMs will become common, accessible even on basic devices.

Wrapping up NLP vs LLM

Understanding differences between NLP and LLM helps you clearly choose the best tools for your goals. NLP covers broader tasks with various methods and simpler tools. LLMs specialize in versatile, human-like language generation with deep learning techniques. AI and blockchain are transforming industries in different ways. If you’re curious about emerging tech, a Blockchain Certification from the Blockchain Council can be a great next step.