Function Calling: Unlock the Power of AI in Your Apps
4 min
Updated May 23, 2025
Estimated reading time: 5 minutes
Did you know that it is possible to supercharge your applications with the power of Artificial intelligence? In this article, we'll explore how Function Calling can open up a world of possibilities, from sending emails to accessing personal databases and fetching real-time data. Get ready to find out how to integrate AI in your projects and unlock the full potential of your creations.
Table of Contents
Limitations of LLM models
These models often face challenges in accessing up-to-date data, performing real-time queries, and performing specific actions. For example, the difficulty in accessing information such as the current weather in a certain city or the current currency exchange rate. Understanding these limitations is crucial to understanding the need to use functions to overcome such obstacles and unlock a wide range of possibilities in integrating AI into applications.
Defining Function Calling
Function Calling is the ability to call a function and manipulate structured data from natural language. For example, when sending text to GPT, it determines whether the question requires a function call. Then, an API is triggered to execute the function and return the response to the user. This allows the conversion of unstructured data into structured data, enabling the execution of specific functions.
GPTs
Firstly, use a GPT from OpenAI (premium plan required) would be the easiest option to perform your functions, visually and using a tool and UI that we are already used to, such as chatGPT.
OpenAI API and wizards
Likewise, a second and third option would also be using the OpenAI ecosystem, as it also offers the ability to make API calls through chat completion, allowing data to be obtained through functions. Additionally, the platform offers the creation of personalized assistants, such as a travel advisor, providing a variety of functionalities similar to creating GPTs.
Google Gemini
Likewise, Google Gemini also offers function execution, with explanatory videos available in the documentation to guide users on how to perform function calls. The platform follows a standard in the JSON file to pass the instructions necessary to execute the functions, providing a clear and standardized approach to using the available features.
Anthropic Claude
Lastly, Claude from Anthropic is another tool that allows us to do function calling. In our training, we offer a detailed approach to how to use Anthropic Claude to unlock the potential of functions in a meaningful way.
Whatsapp example with Function Calling
So, a practical example of the power of enabling functions is the integration of WhatsApp with running functions in a personal database, such as a spreadsheet. Immediately after sending a question via WhatsApp, such as the amount spent on education in a given month, it is possible to activate an action to access and consult the database, providing relevant information. This integration is carried out through tools such as Make and Integromat, which allow the connection between applications and the activation of Function Calling effectively.
Step by Step – Function Calling
Finally, do you want to see a step-by-step guide with a detailed explanation of how function calling works and how it can help you in your application? Watch our full video on YouTube.
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Nos últimos cinco anos, o Hugging Face evoluiu de um chatbot lançado em 2016 para um hub colaborativo que reúne modelos pré‑treinados, bibliotecas e apps de IA; é a forma mais rápida e econômica de validar soluções de NLP e levá‑las ao mercado.
Graças à comunidade vibrante, à documentação detalhada e à integração nativa com PyTorch, TensorFlow and JAX, o Hugging Face tornou‑se a plataforma de referência para adotar IA com rapidez; neste guia, você vai entender o que é, como usar, quanto custa e qual o caminho mais curto para colocar modelos pré‑treinados em produção sem complicação.
Dica Pro: Se o seu objetivo é dominar IA sem depender totalmente de código, confira a nossa AI Agent and Automation Manager Training – nela mostramos como conectar modelos do Hugging Face a ferramentas no‑code como Make, Bubble e FlutterFlow.
O que é o Hugging Face – e por que todo projeto moderno de NLP passa por ele
O que é o Hugging Face – e por que todo projeto moderno de NLP passa por ele?
Em essência, o Hugging Face é um repositório colaborativo open‑source onde pesquisadores e empresas publicam modelos pré‑treinados para tarefas de linguagem, visão e, mais recentemente, multimodalidade. Porém, limitar‑se a essa definição seria injusto, pois a plataforma agrega três componentes-chave:
Hugging Face Hub – um “GitHub para IA” que versiona modelos, datasets and apps interativos, chamados de Spaces.
Biblioteca Transformers – a API Python que expõe milhares de modelos state‑of‑the‑art com apenas algumas linhas de código, compatível com PyTorch, TensorFlow e JAX.
Ferramentas auxiliares – como datasets (ingestão de dados), diffusers (modelos de difusão para geração de imagens) e evaluate (métricas padronizadas).
Dessa forma, desenvolvedores podem explorar o repositório, baixar pesos treinados, ajustar hyperparameters em notebooks e publicar demos interativas sem sair do ecossistema.
Consequentemente, o ciclo de desenvolvimento e feedback fica muito mais curto, algo fundamental em cenários de prototipagem de MVP – uma dor comum aos nossos leitores da persona Founder.
Principais produtos e bibliotecas (Transformers, Diffusers & cia.)
Principais produtos e bibliotecas (Transformers, Diffusers & cia.)
A seguir mergulhamos nos pilares que dão vida ao Hugging Face. Repare como cada componente foi pensado para cobrir uma etapa específica da jornada de IA.
Transformers
Criada inicialmente por Thomas Wolf, a biblioteca transformers abstrai o uso de arquiteturas como BERT, RoBERTa, GPT‑2, T5, BLOOM e Llama.
O pacote traz tokenizers eficientes, classes de modelos, cabeçalhos para tarefas supervisionadas e até pipelines prontos (pipeline(“text-classification”)).
Com isso, tarefas complexas viram funções de quatro ou cinco linhas, acelerando o time‑to‑market.
Datasets
Com datasets, carregar 100 GB de texto ou áudio passa a ser trivial. A biblioteca streama arquivos em chunks, faz caching inteligente e permite transformações (map, filter) em paralelo. Para quem quer treinar modelos autorregressivos ou avaliá‑los com rapidez, essa é a escolha natural.
Diffusers
A revolução da IA generativa não se resume ao texto. Com diffusers, qualquer desenvolvedor pode experimentar Stable Diffusion, ControlNet e outros modelos de difusão. A API é consistente com transformers, e o time do Hugging Face mantém atualizações semanais.
Gradio & Spaces
O Gradio virou sinônimo de demos rápidas. Criou um Interface, passou o modelo, deu deploy – pronto, nasceu um Space público.
Para startups é uma chance de mostrar provas de conceito a investidores sem gastar horas configurando front-end.
Se você deseja aprender como criar MVPs visuais que consomem APIs do Hugging Face, veja nosso FlutterFlow Course e integre IA em apps móveis sem escrever Swift ou Kotlin.
Hugging Face é pago? Esclarecendo mitos sobre custos
Muitos iniciantes perguntam se “o Hugging Face é pago”. A resposta curta: há um plano gratuito robusto, mas também modelos de assinatura para necessidades corporativas.
Gratuito: inclui pull/push ilimitado de repositórios públicos, criação de até três Spaces gratuitos (60 min de CPU/dia) e uso irrestrito da biblioteca transformers. Pro & Enterprise: adicionam repositórios privados, quotas maiores de GPU, auto‑scaling para inferência e suporte dedicado.
Empresas reguladas, como as do setor financeiro, ainda podem contratar um deployment on‑prem para manter dados sensíveis dentro da rede.
Portanto, quem está validando ideias ou estudando individualmente dificilmente precisará gastar.
Só quando o tráfego de inferência cresce é que faz sentido migrar para um plano pago – algo que normalmente coincide com tração de mercado.
Como começar a usar o Hugging Face na prática
Como começar a usar o Hugging Face na prática
Seguir tutoriais picados costuma gerar frustração. Por isso, preparamos um roteiro único que cobre do primeiro pip install até o deploy de um Space. É a única lista que usaremos neste artigo, organizada em ordem lógica:
Create an account em https://huggingface.co e configure seu token de acesso (Settings ▸ Access Tokens).
Faça o pull de um modelo – por exemplo, bert-base-uncased – com from transformers import pipeline.
Rode inferência local: pipe = pipeline(“sentiment-analysis”); pipe(“I love No Code Start Up!”). Observe a resposta em milissegundos.
Publique um Space com Gradio: crie app.py, declare a interface e push via huggingface-cli. Em minutos você terá um link público para compartilhar.
Depois de executar esses passos, você já poderá: • Ajustar modelos com fine‑tuning • Integrar a API REST à sua aplicação Bubble • Proteger inferência via chaves de API privadas
Integração com Ferramentas NoCode e Agentes de IA
Um dos diferenciais do Hugging Face é a facilidade de plugá‑lo em ferramentas sem código. Por exemplo, no N8N você pode receber textos via Webhook, enviá-los à pipeline de classificação e devolver tags analisadas em planilhas Google – tudo sem escrever servidores.
Já no Bubble, a API Plugin Connector importa o endpoint do modelo e expõe a inferência num workflow drag‑and‑drop.
Se quiser aprofundar esses fluxos, recomendamos o nosso Make Course (Integromat) and the SaaS IA NoCode Training, onde criamos projetos de ponta a ponta, incluindo autenticação, armazenamento de dados sensíveis e métricas de uso.
The use of a AI agent for shopping is becoming a strategic necessity for e-commerce companies, purchasing managers and technology and innovation professionals.
This technology makes it possible to automate processes, reduce costs and improve strategic decisions in corporate acquisitions.
Want to understand in detail how these autonomous AI agents work in practice? Check out this detailed article from SAP, which provides concrete examples of how agents select suppliers and generate orders automatically: What are AI agents?.
What is an AI agent for shopping
What is an AI agent for shopping?
An AI agent for procurement is an advanced software designed to automate and optimize processes related to the procurement of goods and services.
It combines artificial intelligence, machine learning, and automation to perform tasks that would normally be done manually.
These agents can act as a virtual assistant for e-commerce, recommending products and facilitating recurring purchases.
Furthermore, they function as a AI chatbot for product recommendation, offering real-time support to managers and internal teams.
How does the application of AI in the purchasing process work?
The application of AI in purchasing mainly involves the automatic collection and analysis of large volumes of data, including purchasing history, supplier behavior, market prices and internal demands.
Want to better understand how these technologies help reduce costs and make more efficient decisions in practice? Check out real examples in IBM's detailed article on How AI optimizes processes in the purchasing sector.
Using this data, the agent suggests ideal suppliers, automatically negotiates better prices, and generates personalized recommendations for new purchases. In addition, it can anticipate future demands and avoid stock shortages, always maintaining ideal supply levels.
Advantages and benefits for companies
Advantages and benefits for companies
Implementing an AI agent brings measurable benefits to organizations:
Cost reduction
Companies report reductions of up to 25% in procurement-related operational costs after implementing intelligent agents. This is due to the automation of manual processes and improved negotiation capabilities through data analysis.
Increased productivity
Intelligent agents reduce time spent on repetitive tasks, allowing teams to focus on strategic activities, increasing productivity by up to 35%. See more details in the article Tips on the benefits of AI in Procurement.
Better strategic decisions
With AI technology to optimize purchasing decisions, companies can make more assertive decisions, based on predictive analysis and historical behavior.
Greater compliance
AI agents also help with compliance by ensuring that all acquisitions follow internal standards and policies, reducing audit risks and fines.
Practical examples and use cases
A retail chain adopted an AI agent to monitor inventory in real time, allowing them to predict demand more accurately. This reduced stockouts and saved thousands of dollars annually.
In the pharmaceutical sector, AI agents automate the renewal of contracts and recurring orders, speeding up administrative processes and reducing manual errors.
Another successful application is in large e-commerces, where agents act by automatically recommending products to customers based on history and preferences, boosting sales.
Want to see how companies like Zara and Coca-Cola are applying AI to their purchasing operations and achieving great results? Read this full report on the DataCamp blog.
Future trends and integration with other technologies
Future trends and integration with other technologies
The future of AI agents for purchasing is highly integrated with other emerging technologies. They already connect to ERP systems, automation platforms such as n8n, Make and generative AI tools such as Dify.
The trend is for these agents to become increasingly personalized and autonomous, creating specific solutions for each company and sector.
This integration promises to make purchasing operations even more efficient and free of bottlenecks. Learn more about trends in Electronic Market.
AI Agent FAQs
How to use AI in the purchasing sector?
To use AI, simply implement an agent connected to the company's current systems, such as ERP and CRM, and allow it to learn from the data.
With this, it can automate purchases, manage suppliers and recommend strategic decisions automatically.
How much does an AI agent earn?
The term “AI agent” refers to the technology, not a specific professional. However, managers who operate these solutions can earn salaries ranging from R$14,000 to R$14,000, depending on their level of experience and responsibility.
What AI agents are there?
The main types are:
Shopping: Automate tasks such as quotation, supplier selection, order generation and inventory control. These agents optimize time and reduce errors in purchasing decisions.
Customer service: responsible for interacting with consumers via chat, voice or email, offering automated support, resolving queries and speeding up service based on the user's history and intention.
Human Resources: They assist in processes such as CV screening, interview scheduling, performance analysis and organizational climate management, promoting greater agility and efficiency in the sector.
Financial management: perform tasks such as bank reconciliation, cash flow forecasting, automatic expense classification and budget control, offering greater precision and agility in corporate finance management.
Customer onboarding: They work on the automated reception of new customers, guiding them through initial processes, such as registration, account activation, explanations about products or services and integration with platforms, ensuring a fluid and fast experience from the first contact.
How much does an AI agent cost?
The cost of implementing an AI agent can vary significantly based on the complexity of the solution and the integrations required.
Popular SaaS platforms like IBM Watson or Pipefy offer plans starting at R$200 per user per month.
Highly customized projects, involving integrations with ERPs, CRMs and intensive use of generative AI, can easily exceed R$20 thousand per month.
If you want an economical and efficient alternative, consider investing in your own training.
NoCode Startup's specialized training teaches you how to develop your own AI agents to automate purchasing processes, customize flows and save money with tailored solutions. Find out how to become an AI Agent Manager here.
Why Your Business Needs an AI Agent Now
In a scenario where efficiency, speed and assertiveness are increasingly required in purchasing areas, having an AI agent is no longer a differentiator but has become a strategic pillar.
This technology transforms the way your company negotiates, anticipates demands and makes critical decisions.
The digital revolution has arrived in full force in the classroom — and now, artificial intelligence (AI) is at the center of this movement. With the growing demand for effective solutions, AI for educators has become one of the most promising areas of educational innovation.
Educators who master these tools not only save time, but can also offer more personalized and effective learning experiences. But after all, what is the best AI for teachers? How can it be applied in everyday school life without complications? And most importantly: how does it directly benefit students?
In this article, you’ll discover the key AI technologies, tools, and agents that are transforming the education landscape — plus practical recommendations you can apply right now.
What is AI in education and why should you, as an educator, understand it?
Artificial intelligence in education refers to the use of algorithms and intelligent agents to facilitate, personalize, or automate teaching and learning tasks. This includes everything from creating lesson plans to monitoring student performance in real time.
AI tools enable:
Reduce time spent on administrative tasks;
Customize activities according to each student’s profile;
Create assessments and interactive content automatically;
Optimize pedagogical planning and classroom management.
Lesson planning: Tools like Canva Magic Write and Curipod are transforming the way educators prepare their lessons. Instead of starting from scratch, simply input a topic or objective and these tools generate a complete teaching structure — with an introduction, development, interactive exercises and conclusion.
This allows for more efficient preparation, saving hours of work. In addition, these resources ensure alignment with curricular guidelines, such as the BNCC, and offer visual and methodological suggestions adapted to the class profile.
Personalization is one of the biggest benefits: the teacher can easily adjust the suggestions to the reality of the classroom and the students' learning level.
Content creation: Generative agents such as ChatGPT, Claude and Eduaide.Ai allow teachers to develop a wide range of pedagogical content quickly and efficiently.
With just a few commands, you can generate explanatory texts on any subject, create thematic summaries, build interactive quizzes with automatic feedback and even script visual presentations for use in the classroom or in remote teaching.
Assessment automation: Correcting and preparing assessments has always required time and attention from teachers — but with the use of AI-based tools, this process becomes much more agile and reliable.
Platforms like Gradescope allow you to upload scanned tests and apply previously defined correction criteria, generating instant results with a high degree of accuracy.
Tools such as ChatGPT can help create essay questions, multiple choice questions or even gamified assessments, based on curricular themes provided by the teacher.
Personalized mentoring: Artificial intelligence enables a much more individualized approach to teaching. By analyzing data on student performance, participation, and behavior, AI tools can identify patterns and learning gaps that would otherwise go unnoticed.
Based on these insights, teachers can provide personalized feedback, propose specific activities for reinforcement, and even adapt the pace and teaching approach according to the needs of each student.
This strengthens the pedagogical bond, increases student engagement and significantly improves academic results — making the learning experience more fair, human and effective.
Types of Artificial Intelligence used in Education
Types of Artificial Intelligence used in Education
Generative AI
Tools like ChatGPT, Claude, and Dify are capable of generating textual and multimodal content (such as images and videos) on demand. They can be used to plan lessons, create teaching materials, or provide alternative explanations for tutoring.
Analytical AI
Solutions like Google Classroom with AI, MagicSchool.ai and ClassDojo monitor student interactions and performance to adapt pedagogical strategies in a personalized way.
Autonomous Educational Agents
Educators can create agents with n8n or Dify to automate tasks like reporting, performance alerts, activity delivery, and more.
AI Agents: The Future of Personalized Education
You Autonomous Agents with AI represent the next level of pedagogical innovation. They are capable of operating continuously and adaptively based on predefined commands and contextual logic.
Usage examples:
Tutor agent to answer students' questions via WhatsApp or Plurall;
Evaluation agent to generate reports per student based on performance on educational platforms;
Content agent who generates new material every week based on the school's curriculum.
O Curipod is a platform that allows you to create interactive classes in just a few minutes with AI support. Teachers can enter a topic and automatically receive a class structure with texts, quizzes, polls, images and other activities. It is ideal for those looking for dynamism and more engaging interactions in the classroom.
Curipod
Canva Magic Write
Integrated with Canva, Magic Write is an AI-powered content generator that helps educators create slides, presentations, summaries, and visual materials in record time. Simply input an idea or topic, and the tool suggests cohesive texts that are visually ready for educational use.
Canva Magic Write
AudioPen
AudioPen automatically converts speech into text, making it ideal for educators who prefer to dictate ideas rather than type. It can be used to create lesson plans, video scripts, educational blog content, and more. It's simple, practical, and fast.
AudioPen
Eduaide.Ai
This tool offers over 100 resources for creating high-quality educational content. From complete lesson plans, study suggestions, personalized feedback to active methodologies — all generated with AI and available in multiple languages. Learn more about Eduardo.AI
Eduaide.Ai
MagicSchool.ai
Platform aimed exclusively at educators, the MagicSchool.ai centralizes the generation of lesson plans, performance reports, quizzes and various content. A true all-in-one dashboard for those who want to increase productivity in pedagogical management.
MagicSchool.ai
Copilot for Education (Microsoft)
O Copilot integrates with Microsoft 365, allowing teachers to automate content creation and administrative tasks. From responding to emails to creating presentations with AI, it is a powerful ally to optimize time in and out of the classroom.
Copilot for Education (Microsoft)
Dify + OpenAI
Ideal for those who want to customize their own educational agents. With Dify, you connect models of the OpenAI into practical workflows — like an agent to review essays, another to grade tests, or even a bot to support students’ parents.
Automation of pedagogical tasks: more time to teach
Tasks such as providing feedback, organizing data, sending notifications, and even correcting tests can be automated. This allows teachers to focus on human interactions, creativity, and close monitoring of students.
There is no single answer, as it depends on the objective. For content creation, ChatGPT and Eduaide.Ai stand out. For lesson planning, Curipod offers a ready-made structure.
For assessment, Gradescope and MagicSchool.ai are good choices. The ideal is to combine tools according to the pedagogical need.
What are the types of AI used in education?
The main types are:
Generative AI (such as ChatGPT and Dify), used to create texts, activities and even videos;
Analytical AI, which interprets student performance and behavior data;
Autonomous agents, who perform educational tasks without constant supervision, such as correcting tests or sending feedback.
What is the best AI website for teachers?
Platforms such as MagicSchool.ai, Eduaide.Ai and Canva Magic Write offer robust solutions for teachers. In the Brazilian ecosystem, No Code Start Up stands out with practical training focused on AI applied to education.
How can AI help teachers?
It helps by automating repetitive tasks, creating personalized content, offering real-time data analysis, and enabling more efficient classroom management. This frees up time and significantly improves the quality of teaching.
AI for Educators is a One-Way Road – And You Need to Be Prepared
AI in education is more than a trend — it’s a transformative reality. Educators who learn to integrate these technologies into their daily lives save time, increase the impact of their work, and improve the quality of teaching.
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