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Firebase or Supabase – Which is Better?

firebase3 nosql modeling

Choosing the backend for your project is one of the most important decisions, but the question always remains which is the best backend: Firebase or Supabase?

Deciding between firebase and supabase can be a difficult task as both backend services are very complete and robust.

Watch the full video about comparing Firebase and Supabase

At the end we will give our opinion on which is the most suitable – Firebase or Supabase.

Firebase as Backend

The benefits of Firebase are diverse. Here are some of them:

  1. Rapid application development: Firebase offers a wide range of out-of-the-box features like user authentication, cloud storage, real-time database, and more.
  2. Scalability: Firebase is highly scalable, meaning your applications can handle a large number of users and concurrent operations without compromising performance. This is especially important for applications that need to handle spikes in traffic.
  3. Cloud Hosting and Storage: Firebase offers cloud hosting for your apps, allowing you to deploy and host your app easily. Additionally, you can store and retrieve data from Firebase Cloud Firestore, a real-time NoSQL database, or Firebase Realtime Database.
  4. User authentication: Firebase Authentication makes it easy to authenticate users to your app by supporting multiple authentication methods such as email/password, social authentication (Google, Facebook, Twitter), and phone authentication.
  5. Data analytics: Firebase provides analytics capabilities that let you track your app's performance, understand user behavior, and make informed decisions based on the data you collect.

These are just some of the benefits of Firebase. It offers a wide range of features and services that can help with the development and growth of your applications.

Supabase as Backend

Supabase offers a number of benefits to developers. Here are some of them:

  1. Powerful Database: Supabase uses PostgreSQL, a highly reliable and robust relational database management system. This means you can take advantage of advanced features like ACID support, complex queries, indexes, and triggers.
  2. Built-in authentication: Supabase provides out-of-the-box authentication capabilities, allowing you to easily add user authentication to your applications. This includes email/password authentication, social authentication (such as signing in with Google), and third-party authentication.
  3. File Storage: With Supabase, you can easily store and manage files such as images, videos, and documents. This is useful for applications that need to handle uploading and storing files for users.
  4. Serverless APIs: Supabase lets you create serverless APIs quickly and easily. You can expose your database data as custom API endpoints, allowing your applications to communicate with the database securely and efficiently.
  5. Real-time updates: Supabase offers real-time update capabilities, allowing your applications to receive instant updates whenever data changes. This is useful for applications that require real-time notifications, such as chats or monitoring dashboards.

These are just some of the benefits of Supabase. It is designed to make application development easier by providing a complete platform for data storage, authentication, and more.

Firebase or supabase, which is better?

See the comparison between the main features of the Firebase and Supabase comparison:

Regarding the market, we see that large, well-known companies use Firebase.

Conclusion – which is better? Firebase or Supabase

firebase

  • Ideal for projects with flexible databases;
  • For Flutterflow, it's still the best option;
  • More robust backend with greater market presence

Supabase

  • Ideal for projects with relational data
  • Easier to learn because it is SQL
  • Ideal for projects with autonomy under code

So, which one will you choose?

To learn more about the tools, see our complete flutterflow course.

If you are interested in Supabase, we have a Supabase course.

Matheus Castelo

Known as “Castelo”, he discovered the power of No-Code when he created his first startup entirely without programming – and that changed everything. Inspired by this experience, he combined his passion for teaching with the No-Code universe, helping thousands of people create their own technologies. Recognized for his engaging teaching style, he was awarded Educator of the Year by the FlutterFlow tool and became an official Ambassador for the platform. Today, his focus is on creating applications, SaaS and AI agents using the best No-Code tools, empowering people to innovate without technical barriers.

Also visit our Youtube channel

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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! 

Artificial intelligence (AI) is transforming the way companies capture leads, interact with customers, drive sales and, consequently, close contacts. Its application offers numerous advantages, simplifying tasks in various areas of a business and promoting increasingly efficient management. 

But how can you use AI for sales strategically? With the right tools, you can automate processes, personalize interactions, and improve your team’s sales performance, even without advanced technical knowledge. In this article, we’ll explore how to integrate AI into your business to generate scalable results and close deals every month.

Understanding the Impact of AI for Sales 

The application of AI to sales has significantly transformed the way companies manage their sales operations. By automating operational tasks and offering optimized solutions, AI improves service agility and accuracy in critical processes, such as lead capture and qualification.

Furthermore, with smart tools, sales teams can meet customer demands in a more structured manner. As a result, they provide a more positive and efficient experience.

It has already been proven that companies that use AI in their sales strategies have seen significant increases in their results. According to research by Harvard Business Review, implementing AI can increase leads by more than 50%, reduce call times by between 60% and 70%, and reduce operational costs by between 40% and 60%.

Therefore, the impact of AI on sales goes far beyond simplifying tasks; it offers consistency in results, becoming an indispensable ally for companies that want to compete in an increasingly dynamic market.

Benefits of using AI for sales

Benefits of using AI for sales

Customer Interaction and Lead Management

AI chatbots provide personalized support 24/7, answering questions, qualifying leads, and collecting information. AI also segments leads and prioritizes those most likely to convert, optimizing team time and increasing campaign efficiency.

Team management and performance analysis

AI also benefits the internal team, analyzing the individual performance of each salesperson and identifying strengths and weaknesses to offer personalized training.

Consumer behavior analysis and data collection

With AI for sales, you can analyze your customers’ buying journey, identify trends and consumption patterns. As a result, you can offer personalized interactions based on each customer’s history and preferences, increasing satisfaction and the chances of conversion.


Sales training and task automation

AI personalizes team training with content and exercises tailored to each salesperson’s needs. This makes it possible to automate repetitive tasks, such as qualifying leads, sending emails, and generating reports, freeing up time for managers and teams to focus on more strategic activities.

Reduction of operational costs

By automating processes and minimizing errors, AI contributes to more efficient resource management, reducing waste and operational costs such as time spent on manual tasks, rework costs and unnecessary administrative expenses.

How to implement AI for sales in your business

1. Identify the main challenges

Before adopting any solution, map out the biggest challenges in your sales process. Do you need to optimize customer service, qualify leads, or automate tasks manuals? By mapping these challenges, it will be easier to direct your efforts, prioritize more critical areas and invest in tools that truly meet your business needs.

2. Choose suitable tools

Currently, the market offers several AI tools for sales, from chatbots for automated customer service to intelligent CRMs that analyze data in real time. Evaluate the available options and prioritize those that best meet the specific needs of your business.

3. Integrate the solutions into your system

Integration with systems such as CRMs (Customer Relationship Management), marketing automation and systems of business management (ERP), it is essential to ensure that AI tools work efficiently alongside existing platforms. Make sure data is connected and accessible to maximize benefits.

4. Train your team

With the training of a good team, artificial intelligence becomes less susceptible to making mistakes, since inconsistencies can be identified and dealt with in a more humane way. 

Train your team to use AI tools effectively to interpret the information they generate. Your sales team will then be prepared to take full advantage of the potential of AI for sales.

5. Measure and optimize results

Finally, there’s no point in implementing AI without monitoring and adjusting processes. Monitor key performance indicators (KPIs) to assess the impact of AI on sales. Analyze results regularly and be ready to make adjustments when necessary to further improve results.

Implementing AI for sales doesn’t have to be a complicated process, especially with so many affordable solutions available. Start with small integrations and scale up as you see results.

AI for Sales: Create Automated Processes Using NoCode Tools

AI for Sales: Create Automated Processes Using NoCode Tools

Creating automated sales processes with AI has never been more accessible, thanks to NoCode tools. Platforms like Make (formerly Integromat), Bubble, and FlutterFlow allow companies to configure intelligent solutions without needing programming knowledge

With these tools, it is possible to customize processes, integrate systems and improve the operational efficiency of the sales team. Furthermore, AI Agent Manager Training from NoCode offers practical knowledge for those who want to implement robust AI solutions, maximizing results and simplifying the commercial routine. 

By combining NoCode tools with specialized training, your company can transform the sales process once and for all, ensuring high performance and closing the best contracts in the digital market.

Conclusion

It is now clear that adopting AI for sales is not only a matter of efficiency, but also of competitiveness in an increasingly dynamic market. Start exploring the possibilities that this technology offers now! Transform your sales process to meet the needs of your Marketing and Sales team.

Want to learn more about how to implement these solutions? Visit the website NoCode Startup and discover courses, tools and tutorials that will boost your company's results!

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