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Lean startup: what is it and why is management simpler?

lean startup planning

Starting a business from scratch can seem very complex. Given the countless planning steps that are suggested, you may feel lost as to which guidance to follow.

But what if I told you that there was a simple way to start your own business?

The concept of lean startup emerges as an innovative approach that challenges traditional methods of business planning and development.

It's a way of create, test and release products and services through process optimization and focus on agile interactions with customers.

In this content, we will delve deeper into the characteristics of this model, presenting the fundamental pillars and the advantages offered to companies.

If you are interested in the subject, be sure to read this content in full.

What is it lean startup and what are its characteristics?

Lean can be translated as “lean”, so, in a free translation, lean startup is technology-based company with high scalability potential, but lean

Thus, while conventional models emphasize the elaboration of detailed plans, lean startup adopts a condensed approach, focused on hypotheses and experiments.

The method aims to reduce resource waste and deliver value to customers, from the beginning.

Continuous experimentation, iterative learning and constant adaptation are highly valued.

And unlike traditional approaches that often involve extensive planning and analysis before launch, lean startup believe in start small and evolve quickly.

What are the principles of Lean Startup?

Below, see the main aspects of this approach: 

What is a Lean Startup?
Lean startup: what is it and why is its management simpler? 4

MVP (Minimum Viable Product)

What is a product's MVP?
Lean startup: what is it and why is its management simpler? 5

The concept of MVP is essential for the proposed lean startup. Instead of spending months or years developing a complete product, companies create a minimum, viable version that contains only the essential features. 

This MVP is then released to the market to collect feedback customers and validate hypotheses. This not only saves resources but also allows the company to obtain insights valuable from the start.

With the use of tools no-code, it is possible to facilitate the construction of the MVP, as they offer greater agility and lower cost.

This saves time and resources to propose, develop and test projects in real time. 

Not afraid to start over

The methodology encourages companies to be agile enough to recognize when something is not working as planned.

If the data and feedback indicate that the product is not doing well or that there is a better opportunity, the company may make a significant change in strategy.

This may involve changes to the value proposition, target audience or even the business model.

Validated learning

Knowledge acquired through experiments and feedback real customers is highly valued. By validating this learning, companies can make more informed decisions and direct their resources more efficiently.

What are the three main pillars of Lean Startup?

Continue reading to learn the pillars of this methodology and its advantages for your startup!

To better understand how the lean startup, it is essential to understand its three fundamental pillars: 

  • customer development
  • agile development 
  • low-cost technology platform

These three elements interact collaboratively to create a framework that saves resources, accelerates innovation, and improves decision-making. Understand more about each of these pillars:

Customer development

The term means customer development In practice, it is based on a proactive relationship with the public, from the beginning of the process. 

In addition to simply listening to customers, lean startup, there is a concern to actively involve them in validating hypotheses and refining the product.

Some ways in which the customer development contributes to the success of the methodology include:

  • Validation of hypotheses: By interacting directly with customers, companies can certify or disprove their assumptions about market needs. This avoids building products that no one wants.
  • Feedback continuous: customers have valuable insight into the product, and its feedback helps shape renewals along the way. Continuously carrying out this process allows the company to better understand and meet market needs.
  • Building Relationships: Customer development focuses on building long-term relationships with customers, enabling loyalty.

Low-cost technology platform

It is considered a fundamental piece to support the methodologyean startup. This type of platform is aligned with the idea of eliminating waste and allocating resources effectively.

A practical example of this pillar is the use of cloud services, such as AWS, Azure or Google Cloud

Based on this pillar, companies evaluate operations as necessary, eliminating the need for large initial investments in infrastructure.

It is worth highlighting that the no-code language makes it possible to create applications and softwares more cheaply.

This is because it is easier and faster to create a functional product without the need to use code, work that is not restricted to professional developers and can be carried out by anyone interested in learning about the knowledge in the area.  

In this way, with the use of open source tools, it is possible to reduce the development costs of software. Process automation allows you to save time and resources.

Agile development

It is the third pillar of lean startup and complements the other two. Focuses on flexibility, adaptation and renewal during the development processthe.

This technique focuses on short cycles, when small parts of the product are developed and implemented. This allows the team to respond quickly to changes and feedback.

Priorities can change as new information emerges. Agile development allows the team to reevaluate and adjust their goals and tasks over time. That way, products are not considered “finished”.

Instead, are always evolvingo, as the team constantly looks for ways to improve them based on feedback customer and market needs.

The no-code tools can be allies in the process. After all, from them, there is greater agility for the development of softwares and applications that the company needs, which contributes to the desired improvement being carried out more quickly and also at a lower cost.

Lean startup advantages

What are the advantages of using the lean methodology?
Lean startup: what is it and why is its management simpler? 6

The methodology allows organizations to strike a balance between innovation and efficiency. This way, they gain advantages such as:

Greater connection with the customer

One of the fundamental principles of lean startup is to have the customer at the center of all decisions. This is not just a nice idea, it is a strategy that can be transformative for business. 

Collect  feedbacks is essential as many businesses fail due to the disconnect between what they think customers want and what they actually want.

When customers see that their opinions matter and that companies are committed to meeting their needs, it creates loyalty. 

Loyal customers not only buy again, but also become brand advocates, recommending it to others.

Better acceptance of products and services

The lean approach to lean startup allows the creation of essential products or services. This means that each element is carefully considered and any unnecessary components are eliminated. 

Eliminating unnecessary elements results in a more focused product or service. Customers appreciate simplicity and ease of use, which can lead to better market acceptance. 

Additionally, leaner products tend to be developed and delivered more quickly, allowing companies enter the market earlier.

Waste reduction

The lean approach eliminates waste time, money and other resources.

By focusing only on what is needed for the MVP and making data-driven decisions, companies reduce the likelihood of investing in directions that will not yield returns.

You resources are directed only to activities that add real value to the product or service. This prevents them from being wasted on projects that have no potential.

Simplified management

Through a focus on experimentation and continuous learning, the lean startup simplifies management. Decisions are based on feedbacks and real data, reducing dependence on uncertain forecasts. 

You may be wondering how to effectively implement these principles in your business and ensure simplified management. Here comes a powerful tool: O bubble.io.

O Bubble is a no-code platform that allows program alone create complete web applications and systems, without the need for prior programming knowledge. And best of all: the No-Code Startup offers a free Bubble course!

This course not only allows you to acquire valuable skills in using Bubble, but also provides a solid foundation for applying the principles of lean startup in your projects.

You will learn how to prototype, test ideas quickly, collect feedback customers and create solutions effectively – all without the need for coding.

Do not miss this opportunity! Find out more about how to create a successful startup!

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Neto Camarano

Neto specialized in Bubble due to the need to create technologies quickly and cheaply for his startup. Since then, he has been creating systems and automations with AI. At the Bubble Developer Summit 2023, he was listed as one of the greatest Bubble mentors in the world. In December, he was named the largest member of the global NoCode community at the NoCode Awards 2023 and first place in the best application competition organized by Bubble itself. Today, Neto focuses on creating AI Agent solutions and automations using N8N and Open AI.

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More Articles from No-Code Start-Up:

The writing revolution has begun, and it’s powered by artificial intelligence. If you’re a writer, copywriter, journalist, screenwriter, or aspiring author, you’ve probably asked yourself: What is the best AI for writing texts, books or improving my writing?

In this comprehensive guide, we'll dive into the 10 Best AI Tools for Writers, exploring how each one can transform your creative process, improve your texts and help with productivity.

Whether you're a beginner or a seasoned author, these platforms offer solutions for different stages of writing: from inspiration to final editing.

Extra tip: If you also want to build your own custom AI writing assistant, check out No Code StartUp AI Agent and Automation Manager Training.

Jasper

O Jasper is an advanced artificial intelligence platform focused on content creation and management at scale.

Much more than a text generator, it combines writing, SEO, team collaboration and brand identity features, making it ideal for writers, copywriters and marketing teams looking for quality productivity.

The tool offers features such as Jasper Chat (for interacting with AI via prompts), SEO Mode (for real-time content optimization), creation of multiple brand voices, and integration with custom workflows.

With a focus on usability and performance, Jasper also stands out for its intuitive interface and for supporting multiple formats: from blogs and emails to scripts and optimized landing pages.

Currently, it is one of the most complete solutions for those who want to use AI to write with consistency, strategy and creativity.

Who is it for: ideal for writers who also work with content marketing or want to automate part of the text creation.

Features:

  • Ready-made templates for different text formats
  • Writing command by prompt
  • Creating a custom brand voice

Plans: from US$49/month.

Sudowrite

Sudowrite
Sudowrite

O Sudowrite is an artificial intelligence tool designed exclusively to assist fiction writers throughout the creative process.

Presented as a true literary co-pilot, it uses generative AI to offer creative insights, unlock writer's block and expand the narrative naturally.

In addition to the traditional suggestions for continuing paragraphs, Sudowrite stands out for its “Wormhole” mode, which suggests several possible alternatives for the next part of the text, and the “Describe” feature, which enhances passages with more sensory descriptions.

Another advanced feature is “Brainstorming,” where the author can explore plot possibilities, conflicts, and characters with suggestions generated by AI. Sudowrite aims to expand the writer’s imagination and accelerate the creative flow without replacing their authorial voice.

Who is it for: book writers, screenwriters and fiction authors.

Features:

  • Generating rich, sensory descriptions
  • Suggestions for continuation of stories
  • “Show, don't tell” mode

Plans: starts at US$10/month.

Writesonic

Writesonic
Writesonic

O Writesonic is an AI-powered writing platform that stands out for its versatility and focus on productivity. Ideal for both writers and marketers, it offers a full range of tools for creating optimized texts, scripts, long-form articles, product descriptions, emails, and sales pages.

The system features a Google Docs-style editor with real-time AI suggestions, as well as support for multiple languages. One of its main features is “Article Writer 5.0,” which allows you to generate SEO-optimized articles based on specific keywords, title, and desired tone.

The platform also has features such as AI-powered image generation, its own chatbot (Chatsonic), and tools for creating high-performance ads. It is a complete solution for those who want to write faster, with better quality and focused on results.

Who is it for: freelance writers and digital content creators.

Features:

  • Blog and Long Article Assistant
  • SEO-focused writing
  • Automatically generate titles, introductions and paragraphs

Plans: free with limitations, paid from US$16/month.

Grammarly

Grammarly
Grammarly

More than just a spell checker, Grammarly is an AI-powered writing assistant that works on multiple layers of text.

It analyzes and suggests improvements in grammar, spelling, punctuation, style, clarity, and even tone of communication. The tool also has features such as a plagiarism checker, sentence rephrasing suggestions, and contextual insights, helping writers adjust their message to the target audience.

Furthermore, the Grammarly It offers a native editor, browser extensions, integration with Word, Google Docs and mobile applications, making it an indispensable resource for those seeking consistency and textual excellence on any platform.

Its AI-based system learns over time and personalizes recommendations based on the user's writing style.

Who is it for: writers who want to raise the level of text revision.

Features:

  • Spelling and grammar correction
  • Suggestions for tone and conciseness
  • Plagiarism Detector

Plans: free, with Premium option starting at US$$12/month.

Rytr

Rytr
Rytr

Rytr is an artificial intelligence platform focused on fast and accessible writing, aimed especially at those looking for agility and simplicity in text production.

With support for 30+ languages and 40+ use case types (such as product descriptions, emails, social media posts, articles, and scripts), Rytr It is widely used by beginner writers, freelancers, and small businesses.

Its intuitive interface allows you to generate content based on simple commands, in addition to having additional tools such as a plagiarism checker, automatic summary and text reformulation.

The system also offers adjustable creativity levels, document history, and integration with third-party applications via API. It is an excellent choice for those who need to generate content efficiently without sacrificing quality.

Who is it for: beginning writers and those looking for a more economical option.

Features:

  • More than 30 text types
  • Rapid generation of ideas and paragraphs
  • Support for over 30 languages

Plans: free with limits, paid from US$9/month.

Smodin

Smodin
Smodin

Smodin is a multifunctional artificial intelligence platform focused on writing, academic research and content generation in several languages, with an emphasis on Portuguese.

Its proposal is to make writing more accessible and efficient, offering tools ranging from automatic writing and paraphrasing to a plagiarism checker, multilingual translator and bibliographic citation generator in formats such as APA and MLA.

Furthermore, the Smodin It has features such as text summarization, answering questions based on reliable sources and generating structured academic content.

It is widely used by students, researchers, teachers and writers who need technical and linguistic support in their texts, whether academic, professional or creative.

Who is it for: students, academic authors and writers who produce in Portuguese.

Features:

  • Automatic writing and paraphrasing
  • Translator and citation generator
  • Plagiarism detector

Plans: starts at R$49/month.

QuillBot

QuillBot
QuillBot

QuillBot is one of the most recognized tools for rewriting and text enhancement with artificial intelligence. Its main feature is the advanced paraphraser, which allows you to reformulate sentences while maintaining the original meaning with variations in tone, flow and vocabulary.

Additionally, the platform offers a complete suite of useful tools for writers, such as a text summarizer, grammar checker, citation generator, spell checker, and translator.

O QuillBot It also offers different writing modes (such as formal, simple and creative), allowing you to adapt the text to the desired style with a simple click.

Its interface is intuitive, and there is integration with Google Docs, Microsoft Word and browser extensions, making it an essential ally for reviews, studies, content creation and editorial productivity.

Who is it for: writers who rewrite and edit large volumes of text.

Features:

  • Paraphrasing with tone control
  • Grammatical correction
  • Extension for Chrome and Word

Plans: Free and Premium from US$9.95/month.

Anyword

Anyword
Anyword

Anyword is an AI-powered content generation tool focused on text performance in marketing and copywriting environments.

Using historical data, conversion predictions, and audience analysis, the platform helps writers create more effective and strategically optimized texts.

One of the main differences of Anyword is its predictive scoring system (Predictive Performance Score), which automatically evaluates which textual variation has the greatest potential for engagement and conversion, based on real data.

The tool allows you to create ads, landing pages, emails, product descriptions and social media posts with a focus on results.

It also offers persona-based personalization, channel-based analytics (Facebook, Google, LinkedIn, etc.), and automated A/B testing, making it ideal for writers who want to combine creativity with data-driven performance.

Who is it for: advertising writers and copywriters.

Features:

  • Text generation with performance prediction
  • Automated A/B testing
  • Suggested variants

Plans: plans starting at US$39/month.

Frase.io

IO Phrase
IO Phrase

O Frase.io is an artificial intelligence platform designed to help writers and content professionals create highly search engine optimized articles.

It combines research, structuring, and writing functionality in one place, allowing users to create relevant content based on deep competitor analysis and search intent.

The system automatically generates briefs with important topics, related keywords and frequently asked questions extracted directly from Google.

Additionally, Frase offers a smart editor with real-time suggestions to improve the SEO of your text, integrations with tools like Google Search Console, and features for creating answers for FAQs and featured snippets.

It's a powerful solution for anyone who wants to write with authority and rank at the top of search results.

Who is it for: blog writers, ghostwriters and content producers.

Features:

  • Automated competitor-based briefings
  • Real-time SEO optimization
  • AI-powered content generation

Plans: start at US$45/month.

Copy.ai

Copy AI
Copy AI

Copy.ai is one of the most complete and accessible platforms for generating content with artificial intelligence.

Created with a focus on simplicity and productivity, it offers more than 90 ready-made text templates for different formats, such as social media posts, product descriptions, emails, video scripts and even ebooks.

The tool also has an intuitive editor and features such as custom workflows and marketing automations.

An important difference is the support for Portuguese and other languages, in addition to the 'Brand Voice' functionality, which allows you to create texts with tonal consistency aligned with your identity.

O Copy.ai It also has collaboration features for teams and integrations via API, making it a robust solution for both individual professionals and marketing and content teams.

Who is it for: content creators in general and writers of multiple formats.

Features:

  • Templates for over 90 text types
  • Writing by simple commands
  • Integration with other tools

Plans: personalized, see here.

What is the Best AI for Writers?

As we have seen, the answer to “What is the best AI for writers?” It depends on your goal: improving style, writing fiction, accelerating productivity, optimizing for SEO, or proofreading for accuracy. The best way to go is to test the tools that best align with your routine.

If you want to master the use of these AIs autonomously, also get to know the NoCode Training with AI from No Code StartUp and discover how to create your own solutions, even without knowing how to program.

Further reading:

Artificial intelligence has advanced rapidly and AI agents are at the heart of this transformation. Unlike simple algorithms or traditional chatbots, intelligent agents are able to perceive the environment, process information based on defined objectives and act autonomously, connecting data, logic and action.

This advancement has driven profound changes in the way we interact with digital systems and carry out everyday tasks.

From automating routine processes to supporting strategic decisions, AI agents have been playing fundamental roles in the digital transformation of companies, careers and digital products.

What is an AI agent?

For an even more practical introduction, check out the AI Agent and Automation Manager Training from NoCode StartUp, which teaches step by step how to structure, deploy and optimize autonomous agents connected with tools such as N8N, Make and GPT.

One AI agent is a software system that receives data from the environment, interprets this information according to previously defined objectives and executes actions autonomously to achieve these objectives.

It is designed to act intelligently, adapting to context, learning from past interactions, and connecting to different tools and platforms to perform different tasks.

How Generative AI Agents Work

According to IBM, generative AI-based agents use advanced machine learning algorithms to generate contextualized responses and decisions — this makes them extremely efficient in personalized and dynamic flows.

Generative AI agents use large-scale language models (LLMs), such as those from OpenAI, to interpret natural language, maintain context between interactions, and produce complex, personalized responses.

This type of agent goes beyond simple reactive response, as it integrates historical data, decision rules and access to external APIs to perform tasks autonomously.

They operate on an architecture that combines natural language processing, contextual memory and logical reasoning engines.

This allows the agent to understand user intent, learn from previous feedback, and optimize its actions based on defined goals.

Therefore, they are ideal for applications that require deeper conversations, continuous personalization and autonomy for practical decisions.

Watch the free video from NoCode StartUp and understand from scratch how a conversational and automated AI agent works in practice:

Difference between chatbot with and without AI agent technology

While the terms “chatbot” and “AI agent” are often used interchangeably, there is a clear distinction between the two. The main difference lies in autonomy, decision-making capabilities, and integration with external data and systems.

While traditional chatbots follow fixed scripts and predefined responses, AI agents apply contextual intelligence, memory, and automated flows to perform real actions beyond conversation.

Traditional chatbot

A conventional chatbot operates on specific triggers, keywords, or simple question-and-answer flows. It usually relies on a static knowledge base and lacks the ability to adapt or customize continuously.

Its usefulness is limited to conducting basic dialogues, such as answering frequently asked questions or forwarding requests to human support.

Conversational AI Agent

An AI agent is built on a foundation of artificial intelligence capable of understanding the context of the conversation, retrieving previous memories, connecting to external APIs, and even making decisions based on conditional logic.

In addition to chatting, it can perform practical tasks — such as searching for information in documents, generating reports or triggering flows in platforms such as Slack, Make, N8N or CRMs.

This makes it ideal for enterprise applications, custom services, and scalable automations.

For an in-depth analysis of the concepts that differentiate rule-based automations and intelligent agents, it is also worth checking out the official MIT documentation on intelligent agents.

Comparison: AI agent, chatbot and traditional automation

To delve deeper into the theory behind these agents, concepts such as “rational agent” and “partially observable environments” are addressed in classic AI works, such as the book Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig.

Types of AI Agents

AI agents can be classified based on their complexity, degree of autonomy, and adaptability. Knowing these types is essential to choosing the best approach for each application and to implementing more efficient and context-appropriate solutions.

Simple reflex agents

These agents are the most basic, reacting to immediate stimuli from the environment based on predefined rules. They have no memory and do not evaluate the history of the interaction, which makes them useful only in situations with completely predictable conditions.

Example: a home automation system that turns on the light when it detects movement in the room, regardless of time or user preferences.

Model-based agents

Unlike simple reflex agents, these maintain an internal model of the environment and use short-term memory. This allows for more informed decisions, even when the scenario is not fully observable, as they consider the current state and recent history to act.

Example: a robot vacuum cleaner that recognizes obstacles, remembers areas already cleaned and adjusts its route to avoid repeating unnecessary tasks.

Goal-based agents

These agents work with clear goals and structure their actions to achieve these objectives. They evaluate different possibilities and plan the necessary steps based on desired results, which makes them ideal for more complex tasks.

Example: a logistics system that organizes deliveries based on the lowest cost, time and most efficient route, adapting to external changes, such as traffic or emergencies.

Utility-based agents

This type of agent goes beyond objectives: it evaluates which action will generate the greatest value or utility among several options. It is indicated when there are multiple possible paths and the ideal is the one that generates the greatest benefit considering different criteria.

Example: a content recommendation platform that evaluates user preferences, schedule, available time and context to recommend the most relevant content.

Learning agents

They are the most advanced and have the ability to learn from past experiences through machine learning algorithms. These agents adjust their logic based on previous interactions, becoming progressively more effective over time.

Example: a virtual customer service agent who, throughout conversations, improves their responses, adapts the tone and anticipates doubts based on the most frequently asked questions.

To understand how the use of AI is becoming a key factor in global digital transformation, McKinsey & Company published a detailed analysis on trends, use cases and economic impact of AI in business.

AI Agent Use Cases
What are AI Agents? Everything You Need to Know 23

AI Agent Use Cases

Companies like OpenAI have been demonstrating in practice how agents based on LLMs are capable of executing complete workflows autonomously, especially when integrated with platforms such as Zapier, Slack or Google Workspace.

The application of artificial intelligence agents is rapidly expanding across various sectors and market niches.

With the evolution of no-code tools and platforms such as N8N, make up, Dify and Bubble, the creation of autonomous agents is no longer restricted to advanced developers and has become part of the reality of professionals, companies and creators of digital solutions.

These agents are especially effective when combined with automation tools, enabling complex workflows without the need for code. Below, we explore how different industries are already benefiting from these intelligent solutions.

Marketing and Sales

In the commercial sector, AI agents can automate everything from the first contact with leads to the generation of personalized proposals.

Through platforms like N8N, it is possible to create flows that collect data from forms, feed CRMs, send personalized emails and track the customer journey.

Additionally, these agents can analyze user behavior and adapt nurturing approaches based on previous interactions.

Service and Support

Companies that handle high volumes of interactions benefit from AI agents trained based on internal documents, FAQs, or databases.

With Dify and Make, for example, you can build assistants that answer questions in real time, automatically open tickets, and notify teams via Slack, email, or other integrations.

Education and Training

In the educational field, agents can be used to guide students, suggest content based on individual progress and even correct tasks in an automated way.

This automation illustrated below shows how AI agents can be practically implemented using N8N. In the flow, we have a financial agent personalized that converses with the user, accesses a Google Sheets spreadsheet to view or record expenses, and responds based on defined logic, allowed categories, and contextual validations.

The agent receives commands like “Show me my expenses for the week” or “Record an expense of R$120 on studies called 'Excel Course'”, and performs all actions automatically, without human intervention.

AI Agent FAQs

What can I automate with an AI agent?

AI agents are extremely versatile and can be used to automate everything from simple tasks — such as responding to emails and organizing information — to more complex processes such as reporting, customer service, lead qualification, and integration between different tools.

It all depends on how it is configured and what tools it accesses.

What is the difference between an AI agent and a customer service bot?

While a traditional bot answers questions based on keywords and fixed flows, an AI agent is trained to understand context, maintain memory, and make autonomous decisions based on logic and data. This allows it to take practical actions and go beyond conversation.

Do I need to know how to program to create an AI agent?

No. With no-code tools like N8N, Make, and Dify, you can create sophisticated agents using visual flows. These platforms allow you to connect APIs, build conditional logic, and integrate AI without having to write a line of code.

Is it possible to use AI agents with WhatsApp?

Yes. With platforms like Make or N8N, you can integrate AI agents into WhatsApp using third-party services like Twilio or Z-API. This way, the agent can interact with users, answer questions, send notifications, or capture data directly from the messaging app.

Why Learn to Build AI Agents Now

AI Agent Manager Training
AI Agent Manager Training

Mastering the creation of AI agents represents a competitive advantage for any professional who wants to stand out in the current market and prepare for the future of work.

By combining no-code tools with the power of artificial intelligence, it becomes possible to develop intelligent solutions that transform operational routines into automated and strategic flows.

These agents are applicable in different contexts, from simple tasks such as organizing emails, to more advanced processes such as generating reports, analyzing data or providing automated service with natural language.

And the best part: all of this can be done without relying on programmers, using accessible and flexible platforms.

Get started today with AI Agent Manager Training, or deepen your automation expertise with the N8N Course  to create agents with greater integration and data structure and take the first step towards building more autonomous, productive and intelligent solutions for your routine or business.

Further reading

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

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

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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