NLP vs. LLMs: Understanding the differences


As AI continues to grow and solve problems across countless industries, a key part of that tech is the ability to seamlessly bridge the gap between human language and machine understanding. This is where natural language processing (NLP) and large language models (LLMs) come in. They provide distinct and specialized approaches for connecting the power of human communication with software and machines.

Or in simpler terms, NLP and LLMs enable us to have human-like conversations with software.

NLP is the translator, analyzing and manipulating human language based on defined rules and structures. This allows machines to comprehend the nuances of grammar, syntax, and context, which enables them to compute sentiment, extract information, and perform machine translation.

LLMs are the brains. Fueled by massive amounts of text data, they can learn to predict and generate language with human-like fluency and adaptability. These advanced models can have conversations, write different kinds of content, and even answer questions in informative and creative ways.

While both NLP and LLMs excel in language processing, they’re actually very different technologies that work in distinct ways. This article delves into the fascinating world of these AI tools, comparing their objectives, techniques, and applications. We’ve broken it down into these topics:

  • What is NLP?

  • LLMs explained

  • Key differences between NLP and LLMs

  • Technological foundations and development

  • Elastic’s solutions in NLP and LLMs

By the end of this post, you’ll understand how they tackle crucial challenges, the limitations they face, and how they shape the future of language interaction with machines.

What is natural language processing (NLP)?

Just like a skilled translator bridges the communication gap between people of different languages, NLP helps machines understand the meaning and intention behind human words. It does this by dissecting the user's input layer by layer. It looks at the grammar, identifies keywords, breaks down sentence structure, and even identifies more nuanced parts of language like sentiment and sarcasm.

By doing these things, it’s able to produce some incredible outputs:

  • Extract key information from massive text data sets, like summarizing news articles or analyzing customer reviews.

  • Chat and interact with humans in a natural way, enabling tools like virtual assistants or chatbots.

  • Translate languages accurately, preserving the nuances of cultural and stylistic differences.

  • Analyze emotions and opinions expressed in text, helping businesses understand customer sentiment or social media trends.

For an in-depth look at NLP, check out What is natural language processing (NLP)?

Large language models (LLMs) explained

LLMs are a completely different technology. Instead of interpreting what’s being asked, LLMs learn directly from massive amounts of text data to build their own internal understanding of the language itself. LLMs can consume data such as books, articles, websites, and more, identifying patterns and relationships in the process. This training allows LLMs to not just understand what you say, but actually predict what you might say next. LLMs can then generate a response or even mimic the user and generate content that follows the same patterns.

This combination of abilities makes LLMs great at:

  • Generating human-quality text: From poems to code, scripts to news articles, LLMs can adapt their writing style to different scenarios, mimicking human creativity in fascinating ways.

  • Understanding complex contexts: Their vast training data allows them to grasp nuance, humor, and even double meaning. This makes their responses feel more natural and engaging.

  • Converse like a person: Instead of pre-programmed responses, LLMs can tailor their conversation based on your questions and past interactions, creating a dynamic and personalized experience.

Want to learn more about specific LLMs like GPT and BERT? Check out What is a large language model (LLM)?

Key differences between NLP and LLMs

Though both technologies are critical to the world of AI and language processing, NLP and LLMs are very different tools. NLP is a form of artificial intelligence with its rules and statistics, which excels at structured tasks like information extraction and translation. LLMs are a type of machine learning model powered by deep learning and massive data. They are the creative maestros, generating text, answering questions, and adapting to various scenarios with impressive fluency. 

Just as they both have their own strengths, they also have their own weaknesses. For example, NLP focuses on accuracy but is far more limited in what it can do in isolation. And while LLMs are far more adaptable, their ability to mimic human expression comes with the risk of carrying over biases from their training data.

Technological foundations and development

Delving deeper, let’s quickly explore the differences in NLP and LLM development. Even though they’re both key parts of bridging the communication gap between humans and machines, technically, they are built in very different ways to solve different problems.

NLP is built on explicit rules and linguistic knowledge. Like an architect meticulously following blueprints, NLP systems rely on predefined rules for grammar, syntax, and semantics. This allows them to excel at tasks with clear structures, such as identifying parts of speech or extracting specific information from text. But these rules can struggle with ambiguity and context, limiting their flexibility.

On the other hand, LLMs don’t rely on rigid blueprints and instead make use of a data-driven approach. They’re not able to be genuinely creative, but guided by patterns and connections from specific data sets, they can estimate a very good impression of creativity. This is why they’re able to generate human-quality text, translate languages creatively, and even have open-ended chats.

Building an NLP system often involves manually setting up rules and linguistic resources, which is a time-consuming and highly specialized process. LLMs, in contrast, rely on automated training on massive data sets, requiring significant computational power and expertise in deep learning techniques.

Application scope and use cases

As we’ve briefly discussed, it is rarely a case of deciding between NLP and LLMs. Often, they go hand in hand as part of a bigger, complete solution. But that doesn’t mean they don’t excel at certain tasks and use cases in different ways:


  • Information extraction: Sifting through data, NLP can isolate key facts and figures, powering market research, financial analysis, and scientific discovery.

  • Sentiment analysis: Gauging customer opinions in reviews or social media, NLP helps businesses understand brand perception and improve customer satisfaction.

  • Machine translation: Breaking down language barriers, NLP enables accurate translation for documents, websites, and real-time conversations.


  • Content creation: From product descriptions to blog posts, LLMs generate engaging content, freeing up human writers for more strategic tasks.

  • Chatbots and virtual assistants: LLMs power conversational AI, enabling natural interactions with customer service bots or virtual assistants.

  • Question answering: Equipped with vast knowledge, LLMs provide insightful answers to complex questions, revolutionizing education and research.

Limitations and challenges

Despite their advancements, both NLP and LLMs have hurdles to clear. NLP can struggle with context and ambiguity, leading to misinterpretations. And LLMs face challenges in understanding nuances, potentially generating inaccurate or even biased outputs. There are also huge ethical considerations with LLMs’ ability to mimic human interactions. This makes responsible development essential to avoid harmful content and remove as many biases as possible from their training data. 

Addressing these limitations requires continuous research, diverse data sets, and careful implementation to ensure both technologies reach their full potential while remaining responsible and ethical.

Elastic’s solutions in NLP and LLMs

While LLMs push boundaries in text generation and understanding, they have their limitations. Accuracy, context sensitivity, and ethical considerations remain crucial questions that aren’t always simple to answer. And this is exactly why we created the Elasticsearch Relevance Engine (ESRE). ESRE is a powerful tool that empowers developers and addresses these challenges, making it easier to create enhanced search experiences.

ESRE unlocks the potential of LLMs while addressing their limitations. Here's how:

  • Enhanced retrieval: ESRE brings you the precision of BM25 text matching and the semantic matching that vector search provides. This powerful combination leads to more relevant and accurate search results, even for complex queries (for example, product codes and descriptions in ecommerce search, or square footage and neighborhood descriptions in property search).

  • Contextual understanding: By integrating with external knowledge bases and NLP pipelines, ESRE empowers LLMs to grasp the context of a search query, leading to more precise and relevant outputs.

  • Mitigating bias: ESRE employs fairness techniques like data selection and model monitoring to reduce bias in LLMs outputs, promoting responsible AI development.

  • Retrieval augmented generation (RAG): Elasticsearch acts as an information bridge in RAG workflows by transferring critical context, such as proprietary data, to LLMs. This provides more relevant answers and fewer hallucinations by providing a more focused understanding of the query.

ESRE goes well beyond just addressing limitations in LLMs. We also provide a rich range of NLP capabilities, such as pre-trained NLP models. These models work out of the box and can help with entity recognition, sentiment analysis, and topic modeling, which combined with the support of LLMs means you can create hybrid search solutions that boast the strengths of both technologies.

Not a choice you need to make

Throughout this article, we've delved into the fascinating technologies of NLP and LLMs. Each of them has their unique strengths and plays their own part in the bigger AI picture. NLP is the rule-follower, great at structured tasks like information extraction and translation. And LLMs are the creatives that excel in content generation and conversations.

But despite the name of this article, it's not actually about choosing one over the other. The true magic lies in bringing them both together: creating an AI tool that uses the meticulous rules of NLP combined with the deep learning of LLMs. This combination unlocks the reality where machines not only comprehend our language but can also engage with it in nuanced and meaningful ways.

And this is precisely where Elastic steps in. With the Elasticsearch Relevance Engine (ESRE), you have the tools to bridge the gap between NLP and LLMs, empowering you to elevate your search accuracy, mitigate bias, deepen your search's contextual understanding, and so much more.

It's not about an "either/or" decision. It's about bringing together the power of NLP and LLMs using the flexibility and tools with Elastic, moving beyond limitations to create search experiences that truly understand and respond to the beautiful nuances of human language.

What you should do next

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