Large Language Models (LLMs) have become one of the most talked-about technologies in recent years. Since the meteoric rise of ChatGPT, generative AI-based tools are being explored by entrepreneurs, freelancers, CLT professionals, and tech-curious individuals.
But why is understanding how LLMs work so important in 2025? Even if you don't know how to program, mastering this type of technology can open doors to automation, the creation of digital products, and innovative solutions in various areas.
In this article, we will explain in an accessible way the concept, operation and real applications of LLMs, focusing on those who want to use AI to generate value without relying on code.
What is an LLM?
LLM stands for Large Language Model. It is a type of artificial intelligence model trained on huge volumes of textual data, capable of understanding, generating and interacting with human language in a natural way. Famous examples include:
- GPT-4 (OpenAI)
- Claude (Anthropic)
- Gemini (Google)
- Mistral
- Perplexity IA
These models function as “artificial brains” capable of performing tasks such as:
- Text generation
- Automatic translation
- Sentiment classification
- Automatic summaries
- Image generation
- Automated service
How do LLMs work?
In simple terms, LLMs are built on Transformer neural networks. They are trained to predict the next word in a sentence, based on large contexts.
The more data and parameters (millions or billions), the more powerful and versatile the model becomes.
Read more: Transformers Explained – Hugging Face
Own LLMs vs. API usage: what do you really need?
Building your own LLM requires robust infrastructure, such as vector storage, high-performance GPUs, and data engineering. That's why most professionals opt for use ready-made LLMs via APIs, like those of OpenAI, Anthropic (Claude), Cohere or Google Gemini.
For those who don't program, tools like Make, Bubble, N8N and LangChain allow you to connect these models to workflows, databases, and visual interfaces, all without writing a line of code.
Additionally, technologies such as Weaviate and Pinecone help organize data into vector bases that improve LLM responses in projects that require memory or customization.
The secret is to combine the capabilities of LLMs with good practices in prompt design, automation and orchestration tools — something you learn step by step in AI Agent Manager Training.
Difference between LLM and Generative AI
Although they are related, not all generative AI is an LLM. Generative AI encompasses many different types of models, such as those that create images (e.g. DALL·E), sounds (e.g. OpenAI Jukebox), or code (e.g. GitHub Copilot).
LLMs are specialized in understanding and generating natural language.
For example, while DALL·E can create an image from a text command, such as “a cat surfing on Mars,” ChatGPT, an LLM — can write a story about that same scenario with coherence and creativity.
Examples of practical applications with NoCode
The real revolution in LLMs is the possibility of using them with visual tools, without the need for programming. Here are some examples:
Create a chatbot with Dify
As Dify Course, it is possible to set up an intelligent chatbot connected to an LLM for customer service or user onboarding.
Automate tasks with Make + OpenAI
Node Makeup Course You learn how to connect services like spreadsheets, email, and CRMs to an LLM, automating responses, data entry, and classifications.
Building AI Agents with N8N and OpenAI
O Agents with OpenAI Course teaches how to structure agents that make decisions based on prompts and context, without coding.
Advantages of LLMs for non-technical people
- Access cutting-edge AI without having to code
- Rapid testing of product ideas (MVPs)
- Personalization of services with high perception of value
- Optimization of internal processes with automations
LLMs and AI Agents: The Future of Interaction
The next evolutionary step is the combination of LLMs and AI agents. Agents are like “digital employees” that interpret contexts, talk to APIs and make decisions autonomously. If you want to learn how to build your agents with generative AI, the ideal path is AI Agent Manager Training.
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