What Are Large Language Models and Why Do They Matter?
This blog explains what Large Language Models (LLMs) are in plain language, showing how systems like GPT-3, ChatGPT, and BLOOM fit together and why they’re more than just chatbots. It highlights how LLMs turn natural language into a powerful interface for software, enable one model to handle many tasks, and act as a new platform for AI-powered products—while also addressing their limitations, such as hallucinations and bias.
1/2/20233 min read
ChatGPT has become the poster child for AI going mainstream—but it’s just one face of a much bigger story. Behind ChatGPT is a family of systems called Large Language Models (LLMs), including models like GPT-3, BLOOM, and others. To understand where this technology is heading, we need to look beyond a single chatbot and understand the underlying engine that powers it all.
What Is a Large Language Model, Really?
A Large Language Model is an AI system trained on huge amounts of text—books, websites, articles, code—to predict the next word in a sequence. That sounds simple, but at scale it becomes powerful. Given a prompt like, “Explain black holes in simple terms,” an LLM doesn’t pull from a fixed FAQ; it generates an answer on the fly, word by word, based on patterns it has learned.
The “large” part refers to both:
Training data size – billions of words from diverse sources
Model size – billions of parameters (internal settings) that encode patterns in language
Because of this scale, LLMs can write essays, summarize documents, translate languages, generate code, and hold multi-turn conversations—all from the same core capability: next-word prediction.
ChatGPT vs GPT-3 vs BLOOM: How Do They Fit Together?
GPT-3 (from OpenAI) is one of the landmark LLMs that kicked off the current wave of interest. It’s a general-purpose text generator: you give it a prompt, and it completes it. Developers have been using GPT-3 since 2020 via APIs to build apps for writing, coding, and more.
ChatGPT is a fine-tuned version of the GPT-3.5 series, trained specifically to be conversational and follow instructions better. It uses an additional process called Reinforcement Learning from Human Feedback (RLHF), where people rate and compare responses, and the model learns to produce more helpful and safer answers. In other words:
GPT-3 is the engine; ChatGPT is that engine wrapped in a chat-focused driving experience.
BLOOM, on the other hand, is an open-source large language model created by the BigScience research community. While GPT-3 and ChatGPT are proprietary, BLOOM’s weights and training details are public, making it a testbed for researchers, startups, and anyone who wants to experiment with LLMs outside closed platforms.
Together, these models show the spectrum: proprietary vs open, general vs fine-tuned, API vs downloadable.
Why Do LLMs Matter?
LLMs are important not just because they’re impressive, but because they change how we interact with software and information:
Natural language as the new interface
Instead of learning menus, commands, and scripting languages, users can simply ask for what they want. “Summarize this contract,” “draft a reply,” “write a SQL query for this table”—these become natural interactions.One model, many tasks
Traditional AI systems are narrow: one for spam detection, another for translation, another for sentiment. LLMs collapse many language tasks into one unified model, which is a huge shift in how we build and deploy AI.Acceleration of human work
LLMs don’t replace expertise, but they compress the tedious parts—first drafts, boilerplate code, repetitive explanations—so humans can focus on review, creativity, and decision-making.Platform for new products
Just as cloud computing enabled a wave of startups, LLM APIs are becoming a new platform layer. Developers can plug models like GPT-3 or BLOOM into their apps to add chat, writing, summarization, and reasoning features without training their own models from scratch.
The Caveats: Power With Limitations
Despite the hype, LLMs are not magic. They:
Can hallucinate—make up convincing but false statements
Reflect biases present in their training data
Don’t “understand” the world; they pattern-match text
Need guardrails, oversight, and good prompt design
Understanding these limitations is crucial to using LLMs responsibly.
Large Language Models like GPT-3, ChatGPT, and BLOOM are not just another tech fad; they’re a fundamental shift in how we tell computers what we want. As natural language becomes a universal interface, LLMs are poised to sit at the center of future tools, platforms, and workflows—quietly transforming how we read, write, code, and create.
