The best Artificial Intelligence tools

January 29, 2024
5 min

Estimated reading time: 8 minutes

no-code is a form of programming that has already revolutionized the development area. Now, imagine what it can do when combined with Artificial Intelligence (AI)?

no-code is a method that allows anyone to create incredible projects, without the need to know how to program. Thus, it is possible to turn a application developer using intuitive and visual tools that simplify and automate the creation process.

AI is the most talked about technology at the moment, which has arrived to change our relationship with machines, capable of performing tasks that would normally require human intelligence, such as recognize images, speak, learn and solve problems.

And as expected, there are already tools that combine the best of both worlds. That’s what we’re going to talk about in this content!

If you want to see our updated video of the best tools, just go here:

Are you curious? Do you want to know how Artificial Intelligence can help you create incredible projects without having to program?

So don't waste time and check out the best artificial intelligence tools to do a no-code job.

What are the best Artificial Intelligence tools for no-code?
Advantages of AI for No-Code Developers
Be a developer with No-Code Start-Up!

What are the best Artificial Intelligence tools for no-code?

There are several Artificial Intelligence tools that facilitate codeless development. But it is important to highlight that its use must be done with caution.

Remember that although AI systems are very advanced, they are still susceptible to failures. Therefore, we recommend a careful look and constant revisions during the process.

Now that you know that, let's get to the point? Discover some of the best generative Artificial Intelligence tools for no-code:

ChatGPT

It is a language model developed by OpenAI, capable of generating texts, based on the context and previous conversations. You can use it to help build content.

Imagine that ChatGPT is an ally to provide ideas and topics, suggest modifications to the text and even review grammar. To earn money with the help of the tool, you can offer services such as:

Creation of content for social networks;
Copywriting;
Business consultancy.

bard

It is an experimental technology from Google that enables collaboration with generative AI. Bard is considered a great tool for brainstorm and accelerate productivity. Its biggest difference is being connected to updated information on the internet, unlike ChatGPT, which only presents data until January 2022.

With Bard, you can generate texts for various purposes, such as creative writing, marketing, education, and entertainment. To earn money with the tool, you can incorporate it into providing services, such as:

Creation and sale of original content;
Text-based learning or entertainment solutions;
Creation and monetization of applications that use Bard as a resource.

Dall-e

It is an AI system that can create realistic and artistic images from a natural language description. With it, it is possible create logos, comics, photorealistic scenes and much more.

Simply put, Dall-e is like an artist who will transform your words into images, using a large set of text-image data. To earn money with its help, you can offer services such as:

Graphic design and illustration;
Animation and digital art;
Editing or manipulating images.

Leonardo AI

It is also a platform aimed at generating images, which allows create visual arts. Its main differentiator in relation to Dall-e is that it offers a greater variety of pre-trained or personalized AI models, which can be adjusted according to the user's preferences.

With Leonardo AI, you can explore different styles, genres and applications and thus join a community of more than 4 million creators. Check out some services that can be done with the tool to earn money:

Creation and sale of visual assets for your projects;
Image editing;
Creation and monetization of applications that use Leonardo AI as a resource.

Eleven Labs

It is a very powerful free online tool, but it should be used with extreme caution. Allows create realistic AI voices, in any language, for various purposes, such as video, games, audiobooks and chatbots.

You can convert text to speech, clone your voice, find and share voices, and use advanced features like text-inserted projects and pauses. See some services that can be offered to earn money using the tool:

Creation and sale of audio content;
Video narration for social networks;
Dubbing or voice change.

HeyGen

It is a tool that turns text into videos with AI-generated avatars and voices. Think of it as a producer who will help you create high-quality videos easily and quickly. Opportunities to make money with HeyGen include:

Creation and sale of high quality videos;
Video production or editing;
Creation and monetization of applications that use HeyGen as a resource.

Voiceflow

It's the collaboration tool through which AI teams design, prototype, and launch conversational experiences. That is, it is a chatbot and voice assistant developer that will help you create incredible conversational experiences for different platforms and customers.

To make money with Voiceflow, you can offer the following services:

Development, integration or consultancy of chatbots and voice assistants;
Training or teaching how to use Voiceflow to create conversational experiences;
Creation and monetization of applications that use Voiceflow as a resource.

It is worth highlighting that it is possible to create and monetize applications that use all of these tools as resources. To do this, you need to understand how to do extra income online creating apps.

Advantages of AI for No-Code Developers

Now that you know the main Artificial Intelligence tools, you may be wondering what advantages they can bring to your work? In this topic, we will show the benefits of using AI:

Time saving

One of the main advantages of using Artificial Intelligence is save a lot of time. You can create projects in minutes or, at most, hours instead of days or weeks.

With AI, you don't have to worry about syntax, bugs, testing, and code maintenance. You can fully focus on your idea and put it into practice quickly.

Quality improvement and error reduction

Another advantage is that Artificial Intelligence makes it easier for the developer to achieve a higher quality in your work.

Think about it, with AI, you can count on the help of the machine, which learns from the data and constantly improves itself. That way, we avoid human errors, inconsistencies and failures.

Increased productivity

Without a doubt, with AI it is possible create more, in less time and this is an excellent advantage when we talk about productivity.

This is because you can also automate processes without knowing programming, such as data collection, cleaning and analysis. Furthermore, it is possible scale your project easily, without needing more resources.

Innovation opportunities

Artificial Intelligence also can open up new opportunities for innovation. With it, it is possible to combine different tools, data and resources to create unique and original projects.

With the time you'll save without having to worry about coding, it's easier to solve complex and challenging problems.

Makes learning easier

One more advantage of Artificial Intelligence is that it can facilitate learning. Imagine that you are taking a programming course and need to learn how to create lines of code, but you don't even know where to start. With AI, It is possible to see, in practice, how the machine works and solves problems.

Be a developer with No-Code Start-Up!

Now that you know the best Artificial Intelligence tools, how about becoming a no-code application developer?

With the Flutterflow course, you learn how to create incredible applications using just your creativity and the Flutter platform, the most popular mobile development framework in the world.

Best of all, the course is 100% free. Do not miss this opportunity. Be a developer, take the free Flutterflow course!

Also visit our Youtube channel

Learn how to create AI Applications, Agents and Automations without having to code

Types of WhatsApp available on the market: discover the options

types of whatsapp available in the market

Before we talk about WhatsApp APIs, it is important to understand the three main types of WhatsApp available:

Personal WhatsApp

WhatsApp Personal is the traditional version of the app, used by billions of people around the world. Intended for individual users, it does not include business-oriented features or automation.

WhatsApp BusinessVersion

Version aimed at small and medium businesses. This version has features such as a product catalog, automatic messages and business profiles, but still relies on manual interactions.

Furthermore, it allows the simultaneous use of two numbers on the same device, one in the personal application and the other in the Business application, in a completely legal manner and in line with Meta's guidelines.

WhatsApp Business API

Unlike previous versions, WhatsApp Business API is not an application, but a cloud-based solution that allows WhatsApp integration to different systems, enabling automation and personalization of interactions.

Companies that manage a large volume of messages use this API to optimize communication with customers, ensuring efficiency and scalability. With this solution, it is possible to:

automate the sending of messages and notifications;
to create chatbots for customer service;
integrate WhatsApp with CRMs, ERPs and other business platforms;
use artificial intelligence to personalize interactions.

How does the WhatsApp Business API work?

The WhatsApp Business API acts as a bridge between different systems, allowing softwares to communicate automatically. Since it is not an application installed on the cell phone, the entire operation takes place in the cloud, ensuring scalability and reliability for companies that need high-performance communication.

Although initially aimed at developers, today there are several solutions that simplify the implementation of the WhatsApp Business API, making it accessible to companies of different sizes.

Now that we understand the concept, let's explore the differences between the Official WhatsApp API and Unofficial WhatsApp APIs.

Official API vs. Unofficial API: Know the main differences

official api vs unofficial api differences

Businesses can choose between two types of WhatsApp API for integration:

Official WhatsApp Business API (provided directly by Meta or licensed companies);
Unofficial APIs (provided by third parties, without any connection to Meta, but within the law).

Next, we will understand the main differences between them.

Official WhatsApp Business API

The Official WhatsApp Business API is provided by Meta (Facebook) itself or by licensed companies. Meta recently began offering this service directly to end users, without the need for licensed intermediaries.

This API can be integrated directly via Facebook's Business Manager (BM), ensuring security and compliance with Meta's policies. Among the main features of the Official API:

integration via Facebook Business Manager;
mandatory use of pre-approved message templates to start conversations;
billing based on conversations initiated;
restriction on sending messages outside of Meta's rules;
lower risk of blocking, as long as you follow the guidelines/

Please note that Meta does not allow unrestricted sending of messages. To start a conversation, it is mandatory to use approved message templates, ensuring that the contact complies with WhatsApp policies. The template categories are:

marketing: promotions, coupons and offers;
utility: order confirmations, delivery tracking;
authentication: sending verification codes;
service: user-initiated messages at no additional cost.

After the first template is sent, the conversation can continue normally for up to 24 hours without the need for a new template.

And how does billing work on the Official API?

The pricing for the Official WhatsApp API is based on conversations initiated and varies depending on the message category:

marketing messages: about R$ 0.36 per conversation started;
utility messages: approximately R$ 0.04 per conversation initiated;
authentication messages: around R$ 0.015 per conversation initiated;
client initiated messages: at no additional cost.

Other important points about billing include the 24-hour validity for each conversation initiated by the company, which means that if it is necessary to continue the interaction after this period, a new paid template will be required.

Additionally, starting in April 2025, authentication and utility messages sent within this 24-hour window will no longer be charged.

Unofficial APIs

Unofficial APIs are solutions offered by third parties, with no direct relationship with Meta. Although they are not licensed, many of these APIs are completely legal and follow security standards. The main characteristics of Unofficial APIs include:

simplified integration via QR Code;
allows sending messages without the need for pre-approved templates;
fixed price per integrated WhatsApp number;
greater flexibility for shipping and automation;
higher risk of ban in case of spam or inappropriate use.

Unlike the Official API, which requires template approval and follows strict rules, Unofficial APIs allow messages to be sent freely, without category restrictions or prior approval. This allows for more dynamic contact with customers, ideal for companies that need freedom in communication.

And how does the billing work? Unofficial APIs?

The pricing of Unofficial APIs varies depending on the provider. Some operate with fixed monthly plans, while others charge per connected WhatsApp number.

some companies offer plans starting from R$99 per integrated WhatsApp number;
others offer packages for multiple numbers, reducing the cost per account;
There are open-source options, which can be used free of charge, but require their own infrastructure.

Despite the freedom offered by Unofficial WhatsApp APIs, it is essential to consider the risks involved. Since there is no direct link to WhatsApp, these APIs are more prone to blocking, especially when used for mass sending without the proper consent of users.

Furthermore, Meta support and warranty are not available, which means that any technical issues or blockages will depend solely on the API provider.

Sending messages without the recipients' authorization can also result in restrictions, compromising the company's continued communication with its customers.

So which API to choose?

Choosing between the Official WhatsApp API and Unofficial WhatsApp APIs depends on your business needs. While the Official API provides greater security and compliance, Unofficial APIs offer more freedom and predictable costs.

Evaluate your options based on your message volume, need for automation, and level of risk you are willing to take.

To learn more about automation and other efficient strategies for integrate chatbots, automatic notifications and artificial intelligence to your business, explore more at our YouTube channel and in the NoCode StartUp website.

February 12, 2025
5 min

Matheus Castelo

How to make an AI Agent trained with my data | RAG

Artificial intelligence is transforming the way we interact with technology, and AI agents are one of the most powerful advancements in this area. However, to make these agents truly effective, it’s essential to train them with data specific to your business.

In this article, we will explore how to create an AI agent using the RAG technique (Retrieval-Augmented Generation) to train models with custom information. You will learn three practical ways to implement this in your own project.

Ready? Happy reading!

What is an AI agent and how does it work with RAG?

Before we get into the practical part, it is important to understand the concept of an AI agent and how it can be improved using RAG.

Basically, an AI agent is a system that can interpret commands, process information, and generate responses autonomously. To do this, it needs three fundamental elements:

AI model: the agent is based on models such as GPT, Llama or Claude, responsible for interpreting and generating text based on learned patterns;
Base prompt: these are the instructions that define how the agent should behave and structure its responses;
memory: essential for AI to remember previous interactions. Some agents have both short-term and long-term memory, allowing the conversation to continue.

In addition to these features, an AI agent can be even more efficient when using the RAG (Retrieval-Augmented Generation) technique, as we mentioned earlier. This means that, instead of relying exclusively on the model's prior knowledge, it can query external databases, such as documents, PDFs, Notion pages, or spreadsheets.

In this way, an agent trained with RAG becomes an expert in specific content, ensuring more precise and contextualized responses.

Method 1: Creating an agent with Dify

Now that you understand the basics, let's get to the practical part: how to create an AI agent trained with your own data!

One of the easiest and most effective ways to create a RAG-trained agent is by using Difyi. This tool allows you to integrate knowledge bases into your assistant quickly and intuitively.

To train your agent at Dify, follow the step by step below:

access the “Knowledge Base” tab within the Dify platform;
upload your documents, such as PDFs, HTML files, spreadsheets or web pages;
Dify processes the files and transforms them into numeric vectors, converting the textual content into a format that AI can interpret efficiently.

This process is known as embedding, in which the tool structures the data on a vector basis, allowing the AI to search and retrieve the most relevant information whenever a question is asked.

Additionally, Dify makes it easy to create virtual databases by organizing knowledge into chunks of information. This way, when a user asks the agent a question, the agent quickly identifies which chunk of text best fits the desired answer.

With Difyi, you can create specialized agents for different purposes, such as:

customer support assistants, who access FAQs and technical manuals;
customer service chatbots, which answer questions about products and services;
sales agents, which use strategic information to personalize approaches.

The best part? Dify automates this entire process behind the scenes, making implementation simple and practical.

Method 2: Creating an agent with OpenAI Assistants and RAG

Another efficient way to train an AI agent with RAG is to use OpenAI Assistants. This solution allows you to create custom assistants, define specific behaviors, and incorporate documents so that the AI can query and respond accurately.

Unlike Dify, which automates much of the process, OpenAI offers greater control over the agent’s settings. To create your assistant using this tool, follow the steps below:

access the OpenAI platform and go to the “Assistants” tab;
create a new wizard, defining a name, description and specific instructions;
choose an AI model, such as GPT-4 Turbo, to ensure more complete and contextual answers;
Upload files that he will use as reference, such as technical manuals, internal documents, or knowledge bases.

When documents are added to the platform, OpenAI transforms this content into a vector database. This way, the agent can consult the information whenever necessary, without relying solely on the model's pre-trained knowledge.

This allows it to provide more personalized and up-to-date responses without requiring a complete AI re-processing. Additionally, OpenAI manages all the infrastructure needed to store and retrieve this information, making it easy to implement for those who don’t want to set up their own database.

One of the main advantages of this approach is its ease of implementation, as OpenAI takes care of the technical part, making the process simple and intuitive. In addition, the model guarantees high accuracy, combining the power of GPT-4 Turbo with specific information about your business, making the assistant much more effective.

If your goal is to create a specialized AI agent without having to set up an advanced technical environment, OpenAI Assistants can be a great choice.

Method 3: Creating an agent with N8N and Supabase

The third way to create an AI agent trained with RAG is by using the integration between N8N and Supabase. This approach allows greater control over the data and optimizes the search for relevant information within the vector database.

While tools like Dify and OpenAI Assistants simplify the process, using N8N in conjunction with Supabase offers more versatility and reduces operational costs by allowing the framework to be fully configured and managed within your own environment.

To create an AI agent trained with this combination, follow the steps below:

configure the vector database in supabase to store the reference documents;
upload the files that the agent will use as a knowledge base, such as manuals, FAQs or technical ebooks;
integrate Supabase with N8N to enable AI to query data and provide contextualized answers;
develop automated flows in N8N to structure agent interactions with users;
optimize agent responses by ensuring that they can access the most relevant blocks of information within the database.

But why use N8N and Supabase? with RAG?

Unlike other solutions, this approach allows for an advanced level of customization and control over the vector database. When a user asks the agent a question, it fetches the most relevant vector of data from Supabase, ensuring that the answer is based on the stored documents.

Additionally, N8N allows you to connect the AI agent to different applications, such as Whatsapp, Slack and Google Drive, expanding the possibilities of use and automation. This flexibility makes the model ideal for companies that need a highly specialized agent.

Among the main advantages of this implementation, the following stand out:

greater control over datas, allowing adjustments and customizations as needed;
cost reduction, as Supabase replaces paid solutions for vector storage;
advanced automation, with intelligent flows and integrations in N8N;
scalability, allowing the knowledge base to grow according to business needs;
greater efficiency, as the agent accesses information directly from the vector database, without relying solely on the AI model.

If you are looking for flexibility and cost reduction, N8N + Supabase is a powerful solution for training specialized AI agents with RAG.

Conclusion

Training an AI agent with your own data is an essential strategy for obtaining more accurate responses aligned with the context of your business. With RAG, you can transform internal files and documents into structured knowledge for AI, optimizing processes and improving the user experience.

If you want to dive deeper into the topic and learn how to create your own AI agents, check out the complete N8N course at NoCode Startup and take your automation to the next level!

February 7, 2025
6 min

Matheus Castelo

What is RAG – IA Dictionary

Imagine you have a super-intelligent assistant trained based on all the knowledge available on the internet. However, when it comes to information specific to your business, it may not have direct references. So, how do you overcome this limitation?

One of the most effective ways to improve your assistant's intelligence is to train it with custom data, such as documents, articles, and internal files.

This technique is known as RAG (Retrieval-Augmented Generation) and allows AI assistants to combine pre-existing knowledge with specific information to provide more accurate and useful answers.

Continue reading to better understand how this approach can transform the use of AI in your business.

How does RAG (Retrieval-Augmented Generation) work?

Now that we understand the concept of RAG (Retrieval-Augmented Generation), let's explore how it works in detail.

Unlike traditional AI assistants that simply generate answers based on previously trained knowledge, RAG searches for information from external sources and combines that data with its prior knowledge to provide more accurate and relevant answers.

The process can be divided into three main steps:

Ask the AI model

The user asks the AI assistant a question, just as they would in ChatGPT or another traditional chatbot.

Information Search (Retrieval)

The AI assistant queries a specific database, such as PDFs, websites, internal documents, or a business knowledge base. It retrieves the most relevant information to answer the question.

Augmented Generation

With the data retrieved, AI refines and structures the response by combining information from the knowledge base with its own linguistic model. This ensures a contextualized, accurate and relevant response.

This method is highly efficient as it allows AI to provide more personalized responses based on internal data. Additionally, the technology can leverage product documentation, support knowledge bases, and even company policies to ensure accurate and relevant information.

However, unlike a conventional chatbot, which responds based only on its original training, a RAG model can be constantly updated with new information, without the need for massive retraining.

In other words, this allows the AI to be highly dynamic and evolve progressively as new content is added, ensuring greater accuracy and relevance in responses.

For example, within the NoCode community, we provide assistants that use RAG to answer questions about tools such as make up, Diff, N8N and Bubble.

Furthermore, these assistants have been trained with specific documentation for these platforms, which allows them to provide even more detailed and accurate answers to students, thus facilitating learning and resolving technical queries.

5 Benefits of using RAG

Now that you understand how RAG works, let's explore the main benefits that this technology can bring to companies and users:

1. More accurate and contextualized responses

RAG enables AI assistants to query up-to-date information in real time, making responses more relevant and detailed.

2. Automation and efficiency

With the ability to access specific knowledge bases, AI reduces the need for constant human support, optimizing time and resources.

3. Continuous learning without the need for retraining

Unlike traditional AI models, which need to be constantly trained and retrained to learn new information, RAG can simply query updated databases.

4. Customization for different businesses

Companies can tailor AI to answer industry-specific questions by training the assistant with technical manuals, internal knowledge bases, and other relevant documents.

5. Applying RAG in customer support

In addition to academic and educational use, companies across a variety of sectors are using RAG to improve customer support.

Imagine a technology company that sells complex softwares. Customers frequently contact support with questions about specific features.

With an AI assistant trained with RAG, a company can feed the AI with its internal knowledge base, technical manuals, and FAQs. This allows the agent to answer questions accurately and quickly, helping to reduce the need for human intervention and streamline customer support.

How to apply RAG in your business?

Companies from different segments can take advantage of this technology to improve internal processes, customer service and task automation. Below, check out some practical strategies for applying RAG to your business.

1. Identify your company's main needs

Before integrating RAG, evaluate which areas of your business can benefit from this technology. Ask yourself the following questions:

Does customer support receive a lot of repetitive questions?
Does your team need to access technical documents frequently?
Is there a large database that could be better utilized?
Could internal training be optimized with an AI assistant?

2. Choose the right data sources

The great advantage of RAG is its ability to search for information from external sources. To ensure accurate and reliable answers, it is essential to select the best data repositories. Some options include:

technical documentation and product manuals;
FAQs and internal knowledge bases;
articles, research and case studies;
structured data from CRMS and ERPS;
pdf files, spreadsheets and notion.

3. Integrate RAG with your existing tools

For best results, RAG should be connected to the platforms your team already uses. Some ways to integrate include:

Chatbots and virtual assistants: AI trained to answer recurring questions and provide technical support;
Management systems (CRM/ERP): AI can access customer data to provide more personalized responses;
E-learning and corporate training: intelligent assistants that help employees access learning materials quickly;
E-commerce and customer service: chatbots that check inventory, return policies and product recommendations.

4. Evaluate and optimize

Implementing RAG doesn’t end with the initial setup. It’s essential to monitor AI performance by analyzing metrics such as:

response accuracy rate;
user satisfaction;
reduction of service time;
most frequently asked questions and opportunities for improvement.

With this information, you can improve the AI database and ensure that the answers become increasingly accurate.

Conclusion

Whether it’s to improve customer support, automate processes or optimize internal knowledge management, RAG is a powerful and affordable solution for companies in different segments.

With this technology, AI agents can access specific knowledge bases, improve the user experience and reduce the need for extensive training.

If you want to learn how to create intelligent AI assistants using N8N, check out NoCode Startup's complete course. In it, you will have access to practical training on automation and data integration to make your business' AI even more efficient.

Explore more about the N8N Course – NoCode Startup and start transforming your company with artificial intelligence!

February 3, 2025
5 min

special start of the year offer

The best Artificial Intelligence tools

Table of Contents

What are the best Artificial Intelligence tools for no-code?

ChatGPT

bard

Dall-e

Leonardo AI

Eleven Labs

HeyGen

Voiceflow

Advantages of AI for No-Code Developers

Time saving

Quality improvement and error reduction

Increased productivity

Innovation opportunities

Makes learning easier

Be a developer with No-Code Start-Up!

Watch our Free MasterClass

No-Code Start-Up

Learn how to create AI Applications, Agents and Automations without having to code

More Articles from No-Code Start-Up:

How WhatsApp Business API Works: Official vs Unofficial

Types of WhatsApp available on the market: discover the options

Personal WhatsApp

WhatsApp BusinessVersion

WhatsApp Business API

How does the WhatsApp Business API work?

Official API vs. Unofficial API: Know the main differences

Official WhatsApp Business API

And how does billing work on the Official API?

Unofficial APIs

And how does the billing work? Unofficial APIs?

So which API to choose?

How to make an AI Agent trained with my data | RAG

What is an AI agent and how does it work with RAG?

Method 1: Creating an agent with Dify

Method 2: Creating an agent with OpenAI Assistants and RAG

Method 3: Creating an agent with N8N and Supabase

But why use N8N and Supabase? with RAG?

Conclusion

What is RAG – IA Dictionary

How does RAG (Retrieval-Augmented Generation) work?

Ask the AI model

Information Search (Retrieval)

Augmented Generation

5 Benefits of using RAG

1. More accurate and contextualized responses

2. Automation and efficiency

3. Continuous learning without the need for retraining

4. Customization for different businesses

5. Applying RAG in customer support

How to apply RAG in your business?

1. Identify your company's main needs

2. Choose the right data sources

3. Integrate RAG with your existing tools

4. Evaluate and optimize

Conclusion

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Community

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