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8 mistakes when using no-code: how to avoid them when creating your app

programmer in the office

Estimated reading time: 8 minutes

The market for codeless programming is on the rise and is even considered one of the hottest trends in recent years in the IT area. no-code, as it is known, helps democratize application creation by being an accessible method for anyone who wants to learn. 

Although, errors when using no-code They are common for those who are starting out and can go unnoticed if there is no close eye on the process. 

Certainly, no-code is a revolution for entrepreneurs and startups who want automate processes without knowing programming. They exist no-code platforms with the most diverse functionalities and templates ready to be customized. However, to achieve create effective applications, learning is essential and in this aspect many people end up making mistakes.

no-code is worth it because it is easier to learn than traditional programming, but it is still necessary to study and stay constantly updated. 

If you are starting out and are curious to know the main errors when using no-code, keep reading, as we will clarify all your doubts. 

Good reading! 

woman-working-office

What are the most common errors when using the no-code platform?

THE No-Code Start-Up separated the 8 most common mistakes that beginners face when starting to use no-code platforms. Stay with us until the end of reading to understand everyone and avoid falling into the same situations! 

Lack of knowledge of the tool

A primary mistake that many no-code beginners make is underestimating the importance of knowing the tool they are using. Imagine trying to program in a traditional way without knowing the main languages. This scenario seems disastrous, doesn't it?

However, starting to use a no-code platform without knowing its features follows the same principle. Lack of knowledge about the tool can lead to technical errors, in addition to limit your creative capabilities

Many people think that because no-code is simpler, there is no need to study the platforms, as everything will already be ready for use. In fact, it is essential to explore its features and functionalities to make more informed decisions and optimize the development process. 

To avoid frustration, invest time in learn about your chosen platform - The FlutterFlow, for example. This way, you will be better prepared to create high-quality applications without going through unnecessary hassle. 

Abandoning basic IT knowledge

no-code has become popular as an affordable solution for creating applications without the need for complex programming. And really, these tools allow the developer create in a much simpler way. However, it is not a good idea to completely abandon basic information technology (IT) knowledge. 

Understanding the principles of the IT area is very important, even in the no-code universe. This is because knowledge related to:

  • Performance optimization;
  • Complex integrations;
  • Data security;
  • Regulatory compliance;
  • Scalability.

So, believing that no-code is a solution that requires no basic understanding of IT is a mistake. Without this knowledge you may fall into decisions that result in performance issues, incompatibility with other solutions or security vulnerabilities. 

Not having skills for the sector

Another common mistake for those starting out in no-code application development is not having the necessary skills to work in the sector. Application developers, whether no-code or traditional, need to improve some skills, as:

  • Programming logic;
  • Knowledge of programming languages;
  • Understanding banking and data modeling;
  • Understand UI/UX Design;

Requirements may vary depending on the platform and type of application being created. Another important skill is understanding that a successful app is not just about technology but also involves understand users' needs. We'll delve deeper into this later. 

Not prioritizing apps for business

You already understand that no-code allows the creation of a wide variety of applications, but that using it strategically is also essential. And this involves directing your efforts towards projects that have a clear purpose and potential for financial return.

Think about it, would you like to develop applications without a goal? It is certainly not a good option and can waste time and resources.

Ask yourself: 

  • How can my app solve a specific problem for users? 
  • What added value will it bring? 
  • Is there a real market demand for this application?

It is also important to take into account how your app will be monetized, whether through subscriptions, advertising or sales. If you want your app to be a sustainable enterprise, monetization must be studied.

Not studying the market

As we have already discussed above, studying the market is very important when starting to develop any application. Neglecting market research can be shooting yourself in the foot, as the app may simply not have a place in the market and end up failing. 

To do a good market analysis, you need: 

  • Understand the competition;
  • Analyze industry trends;
  • Identify unmet user needs;
  • Understand the target audience;
  • Assess potential monetization.

By carrying out this study, it becomes easier identify market gaps and untapped opportunities. This way, you can develop the concept of your app, its features and even your marketing strategy.

Adopt many tools

Another very common mistake among no-code beginners is adopting too many tools simultaneously. You can try multiple platforms, but we recommend that your focus on one at a time. If you get lost in the countless possibilities available, this can lead to unnecessary complexity. 

Each no-code platform has its own unique interface, logic and feature set. Applications created with Bubble, for example, can be an excellent starting point, as they allow: 

  • Creation of practically any application;
  • Have Frontend and Backend in one place;
  • Make apps responsive web and apps PWA;
  • Perform advanced integrations with APIs;
  • Create scalable apps with performance.

Are you interested? How about starting today? free Bubble course

Have complex ideas for apps

Many people wonder if it is possible to develop complex apps with no-code and the answer is yes. However, most of them are beginners, without any significant experience. So, we want to give you some advice: start simple and then try to build more advanced projects. 

The no-code platforms are very efficient, but they are not powered by magical forces. That is, it is necessary start with simple projects so that you become familiar with the features gradually. As you gain experience, you will be better prepared to turn your complex ideas into reality. 

Don't take serious courses and only use free tutorials

The last most common mistake when talking about no-code involves relying exclusively on free tutorials and avoiding investing in paid courses. But don't get us wrong, free tutorials can be very useful and save you in many situations. However, they tend to be more based on basic and specific knowledge. 

On the other hand, courses are designed to offer a more complete and structured education. For example, in our Bubble training complete, there is a method in which we cover topics in an organized way. We also like to have our hands dirty, creating real applications and testing any problems that may appear. 

And, believe me, this method makes all the difference to your learning. Having experienced instructors and dedicated support ensures you can get answers to your questions, personalized guidance, and constructive feedback from experts in the field. 

Therefore, consider investing in a good course to start a no-code developer career

Advantages of using no-code correctly

Using the no-code correctly, following all the learning steps, can bring many advantages. Shall we see some of them? 

  • Deeper learning;
  • Greater confidence when putting it into practice;
  • Time saving;
  • Lower long-term cost.

By avoiding all the mistakes we have mentioned, you have the ability to become a true expert in no-code technology. Just remember to be patient and don't try to skip any steps. 

Count on No-Code Start-Up!

no-code could be what you needed to turn your ideas into reality and automate your startup's processes. Learning more about this methodology is simple and quick with No-Code Start-Up. 

And if you haven't developed your company yet, we can also help you through no-code. 

Do you want to create a star up? See how!

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The WhatsApp API is one of the main solutions for companies that want to scale their customer service and communication automation. 

WhatsApp, being one of the most popular messaging apps in the world, allows direct and efficient interactions with customers. However, when it comes to automation and integration, using the WhatsApp API becomes essential.

So, there are two main options for this integration: the Official WhatsApp Business API and the Unofficial APIs. But which one should you choose? 

In this article, we’ll explore the differences, advantages, disadvantages, pricing models, and costs to help you make the best decision for your project. Keep reading and find out which WhatsApp API makes the most sense for your business!

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?

which whatsapp 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.

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?

What is an AI agent and how does it work?

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

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

method 2 creating an agent with openai assistants

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

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!

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?

How does RAG 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.

how does rag generation increase work

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

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! 

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