En este artículo encontrarás una breve descripción de los temas que hemos abordado en este webinar como: qué es la IA generativa, cómo funciona y qué herramientas puedes usar hoy mismo para aprovecharla, con demos reales, casos prácticos y enlaces directos para que puedas probarlo.

Qué es la IA Generativa y cómo funciona

La IA generativa es un tipo de inteligencia artificial capaz de crear contenido nuevo —texto, imágenes, audio, video o código— en lugar de limitarse a analizar datos existentes.

Modelos como GPT-4, DALL·E 3, Copilot y Azure AI permiten:

• Generar artículos, guiones y resúmenes automáticamente.

• Crear imágenes e ilustraciones a partir de descripciones.

• Traducir y doblar videos en múltiples idiomas.

• Escribir y depurar código en tiempo récord.

💡 Dato clave: ChatGPT alcanzó 100 millones de usuarios en solo 3 meses, superando a tecnologías como internet y smartphones.

Microsoft y la evolución de la IA

Microsoft lleva décadas integrando inteligencia artificial en sus productos:

• 1990s: Primeras interacciones (como Clippy).

• 2010: Kinect revoluciona la interacción por movimiento.

• 2019: Alianza con OpenAI para acelerar la IA generativa.

• 2023-2025: Expansión de Copilot a Office, Visual Studio y Azure AI Foundry.

Herramientas de IA Generativa en Azure

Si buscas usar IA generativa en español y con soporte empresarial, Azure ofrece un ecosistema potente y seguro.

1. Avatares TTS (Text-to-Speech)

• Avatares realistas que hablan y gesticulan en tiempo real.

• Compatible con GPT-4o y múltiples idiomas.

• Casos de uso: atención al cliente, capacitación, videos corporativos.

2. Traducción automática de videos

• Convierte videos a otros idiomas manteniendo la voz original mediante clonación de voz (con consentimiento).

• Ideal para creadores de contenido, educación y empresas globales.

3. Azure AI Foundry

• Plataforma para crear soluciones de IA generativa personalizadas.

• Compatible con modelos como GPT-4o, DALL·E 3 y más.

• Integración con tus datos para respuestas precisas y seguras.

4. Copilot Studio

• Construye chatbots y asistentes para Microsoft 365, Teams, Facebook o Slack.

• Ideal para empresas que quieren soluciones rápidas sin programar.

Modelos multimodales: más allá del texto

La nueva generación de modelos, como GPT-4o multimodal, entiende y procesa texto, imágenes y voz en conjunto.
Esto permite:

• Analizar documentos con gráficos e imágenes.

• Crear publicaciones y contenido visual a partir de fotografías.

• Asistir en soporte técnico con imágenes de referencia.

IA Responsable y seguridad de contenido

Microsoft implementa herramientas de Content Safety para detectar contenido sensible o protegido por derechos de autor, asegurando un uso ético y responsable de la IA.
Esto es clave para empresas que buscan cumplir regulaciones y proteger su marca.Por qué la IA Generativa es clave en 2025

• Aumenta la productividad: automatiza tareas creativas y repetitivas.

• Amplía el alcance: traduce y adapta contenido a nuevos mercados.

• Impulsa la innovación: permite experimentar con nuevos formatos y experiencias.

📺 Mirá la sesión completa aquí (en español): Ver en YouTube

🔗 Conectemos en LinkedIn: Pablito Piova - LinkedIn

💬 Pregunta para ti:
¿Cuál sería tu primer proyecto si pudieras integrar IA generativa en tu negocio o trabajo?

Early in the year, I participated in the CxO Networking Dinner, thanks to South Florida Tech Hub 🌴 and the OZ team, where I shared the stage with industry leaders to discuss the impact of AI in business. Later, I had an unforgettable reunion during my second visit to the MVP Summit in Redmond #MVPBuzz, strengthening ties with colleagues and friends at Microsoft in the exciting world of exciting world of AI, Azure, Power Platform and more.

Mid-year, I was recognized as a Microsoft MVP in AI for the third consecutive year, an honor that drives me to keep promoting AI adoption. During this period at OZ, we worked intensively on projects involving Generative AI, Azure AI, Azure OpenAI, Azure AI Search, Copilot Studio and TTS Avatar, developing POCs and presentations to offer innovative solutions to our clients.

A major highlight came at the AI Future Summit – South Florida 2024, where we showcased success stories in collaboration with #Microsoft, demonstrating the real value #AI can bring to various sectors. I also experienced a notable increase in my LinkedIn following, surpassing 5,000 followers, and had the chance to share my knowledge at global communities, including two AI-focused series at Microsoft Reactor.

To top it all off, I participated in Microsoft Ignite #MSIgnite as a Lab expert, collaborating with exceptional Microsoft professionals and showcasing the projects we developed throughout 2024.

I want to express my deepest gratitude to Jason Milgram , who not only extended the initial call for this journey at #FollowOZ but has also been a true mentor every step of the way. I’m also grateful for the constant support of Emmanuel Ramos and Sal Cardozo , who are always ready to offer valuable ideas, provide assistance, and place their trust in our endeavors.

My thanks extend to the Microsoft Most Valuable Professional and, in particular, to the colleagues and friends with whom I shared events throughout 2024, fostering AI in various corners of the world. Above all, I want to recognize my family for their unwavering support in this incredible journey, fueling my motivation and energy to keep moving forward.

Looking Ahead to 2025

The coming year promises to be even more exciting. We will continue developing AI and Generative AI solutions for key clients, expanding our innovation initiatives, and seeking new opportunities to share knowledge at events and across communities.

This is just a glimpse of my 2024. I’m immensely grateful to my family, my colleagues at OZ Digital Consulting, and the Microsoft community for their unwavering inspiration. See you in 2025 with more AI and innovation!

This article was originally published on LinkedIn.

But for me, Ignite was more than just attending sessions. I was honored to participate as an expert in Lab 402: Interacting with multimodal Generative AI models, helping attendees explore how to work with cutting-edge AI technologies like GPT-4o through Azure OpenAI.

The Expert Lab Experience – Lab 402: Interacting with multimodal Generative AI models

Lab 402 was an immersive, hands-on learning experience where participants explored how to design, implement, and optimize multimodal AI solutions. Using Azure AI Foundry (formerly AI Studio) and GPT-4o, attendees learned how to build intelligent systems that could process text, images, and other input types simultaneously.

My role as an expert was to guide participants, answer questions, and share best practices for integrating multimodal AI into real-world applications. From experimenting with Azure AI Playgrounds to connecting generative models with enterprise data, the energy in the room was pure innovation.

Deep Dive into AI Search – BRK105 Azure AI Search: RAG for better results, larger scale, faster answers

In addition to my lab involvement, I attended BRK105, a breakout session on Azure AI Search and Retrieval-Augmented Generation (RAG). This session showed how combining large language models with relevant, high-quality search results can produce faster, more accurate answers at scale.

Key takeaways:

• Scalability: Azure AI Search can handle massive datasets while maintaining performance.

• Precision: RAG ensures AI answers are grounded in relevant information, reducing hallucinations.

• Enterprise readiness: Integration with Azure security and compliance frameworks makes it viable for sensitive environments.

MVP’s Recommended Microsoft Ignite 2024 Session | Microsoft Community Hub

Microsoft Ignite 2024 AI Highlights

This year’s Book of News revealed Microsoft’s ambitious vision for AI, and several announcements stood out for me:

• AI Agents: Autonomous AI systems that can handle complex workflows like returns processing or invoice approvals without constant user prompts.

• Custom AI Chips: New Microsoft-designed infrastructure chips to accelerate AI workloads while improving energy efficiency and security.

• Windows 365 Link: A new mini-PC, launching April 2025, that connects directly to Windows 365 without local storage.

• Microsoft 365 Companions: A new productivity layer in Windows 11 that integrates contacts, files, and calendar directly into the taskbar.

You can explore all announcements in the official Microsoft Ignite 2024 Book of News.

Why This Year’s Ignite Felt Different

While the excitement of brand-new AI models may have slowed compared to last year, Microsoft’s focus in 2024 was on making AI practical, multimodal, and ready for real business scenarios. From Copilot integrations to autonomous agents and more immersive AI development tools, the shift is toward operationalizing AI.

Being both a participant and an expert gave me a unique perspective: I got to see the curiosity and creativity of attendees firsthand while also connecting the dots between Microsoft’s announcements and the real-world challenges companies face.

Final Thoughts

Microsoft Ignite 2024 was an inspiring reminder that we’re entering a new era of intelligent applications — powered by multimodal AI, scalable AI search, and deeply integrated productivity tools.

For me, guiding others in Lab 402 and learning from sessions like BRK105 was not just professionally rewarding, but a chance to contribute to shaping the AI-powered future.

https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:7265502407004979200?

I’m already looking forward to applying what I learned — and sharing more insights in the months ahead.

At the recent AI Tour, Microsoft introduced a groundbreaking advancement in artificial intelligence: autonomous agents in Copilot Studio, set to transform how businesses manage tasks and scale productivity. This new tool allows teams to create custom agents that independently handle complex tasks, optimizing efficiency and enhancing the customer experience.

What Makes These Agents Stand Out?

As Jared Spataro highlighted in his presentation and blog post New Autonomous Agents Scale Your Team Like Never Before, these autonomous agents excel at both automation and personalization. Spataro’s blog explains how this technology represents a leap forward, providing companies with a way to significantly reduce administrative overhead and boost efficiency. These agents work autonomously and can also integrate with Copilot, allowing for effective collaboration where humans can step in as needed.

Success Story: McKinsey & Company

During the AI Tour, McKinsey & Company was featured as a real-world example of these agents in action. Using Copilot Studio, McKinsey developed an autonomous agent that processes emails from prospective clients, identifies the context, and routes tasks to the appropriate personnel—achieving a 90% reduction in response time and a 30% cut in administrative costs.

Automation Without Limits One of the most exciting aspects of these agents is their ability to manage multiple interactions simultaneously. As showcased in the launch video, autonomous agents can handle hundreds of processes at once, providing companies in any industry with a tool to automate and optimize workflows, greatly enhancing operational efficiency.

The Future of Productivity at Your Fingertips

With these autonomous agents, Microsoft is not only making advanced AI accessible but also empowering teams to be more agile and effective in their processes. From customer service to internal operations, this technology redefines business productivity and is accessible to any organization looking to scale its capabilities. The future of work and productivity is here with Copilot’s autonomous agents!

As an additional resource, check out Satya Nadella’s AI Tour Keynote: London, where Microsoft unveils new capabilities to drive AI-first business processes. Next month, Copilot Studio’s autonomous agent creation will enter public preview, alongside ten new Dynamics 365 agents for enhancing sales, service, finance, and supply chain teams.

For a deeper look at this announcement and many other innovations, join us at Microsoft Ignite. Visit Microsoft Ignite to learn more and register for the event.

—This article was originally published on LinkedIn.

¿Qué es RAG y por qué es importante?

RAG (Generación Aumentada por Recuperación) es un enfoque que amplía el conocimiento de un modelo de lenguaje conectándolo a fuentes de información externas, como bases de datos vectoriales o motores de búsqueda.
Esto permite:

• Consultar información más reciente que la fecha de corte del entrenamiento del modelo.

• Integrar conocimiento privado o especializado.

• Personalizar las respuestas a un dominio concreto.

Sin RAG, un modelo de lenguaje solo puede responder con la información que tenía en el momento de su entrenamiento.

Breve repaso: cómo funcionan los modelos de lenguaje

Antes de entender RAG, es clave saber cómo trabajan los Large Language Models (LLMs):

• Utilizan arquitecturas tipo Transformer para procesar secuencias de texto.

• Funcionan prediciendo el siguiente token (unidad de texto) en función del contexto previo.

• A mayor contexto y estructura, mayor coherencia y relevancia en las respuestas.

Con el tiempo, estos modelos han adoptado formatos de interacción más naturales, como el chat completion, que permiten mantener un historial de conversación y generar respuestas más contextuales.

Limitaciones que RAG resuelve

Los modelos de lenguaje destacan en comprensión y generación de texto, pero enfrentan dos grandes problemas:

1. Conocimiento desactualizado
   Si el entrenamiento terminó en 2023, no podrán responder sobre hechos ocurridos en 2024 o posteriores.

2. Desconocimiento de información interna
   No saben nada sobre datos privados de una empresa o proyecto a menos que se los incluyas explícitamente.

Estrategias comunes para solucionarlo

• Prompt Engineering: incluir la información relevante directamente en la consulta.

• Fine-tuning: reentrenar el modelo con datos adicionales (costoso y menos flexible si la información cambia con frecuencia).

• RAG: buscar datos en tiempo real y pasarlos como contexto al modelo, sin modificar su entrenamiento original.

Cómo funciona RAG paso a paso

El flujo básico de RAG es el siguiente:

1. El usuario formula una pregunta.

2. El sistema busca información relevante en una base de datos vectorial (Azure Cognitive Search, Pinecone, Qdrant, etc.).

3. Los resultados se incorporan como contexto en el prompt.

4. El modelo de lenguaje genera la respuesta final con ese contexto.

Ventajas clave de RAG

• Actualización continua: se conecta a datos recientes.

• Adaptación al dominio: integra información interna o especializada.

• Multilingüismo: funciona con distintos idiomas sin reentrenar.

• Versatilidad de formatos: compatible con texto, código, imágenes y más.

Ejemplos prácticos de RAG

En la sesión se mostraron casos que van desde lo simple hasta lo avanzado:

• Uso de modelos locales para ahorrar costos en la nube.

• Creación de bases vectoriales en memoria y consultas en varios idiomas.

• Integración con datos estructurados (bases SQL, Postgres) y no estructurados (documentos, archivos).

• Técnicas de query rewriting para mejorar la precisión de búsqueda antes de pasar la información al modelo.

RAG en acción: oportunidades para desarrolladores

Este patrón abre múltiples posibilidades:

• Asistentes virtuales que acceden a documentación interna.

• Chatbots con información actualizada de productos o servicios.

• Herramientas de búsqueda inteligente en repositorios de código.

• Sistemas de soporte técnico con acceso a bases de conocimiento.

Hackatón RAG: lleva la teoría a la práctica

Para impulsar el aprendizaje y la experimentación, se lanzó un hackatón de dos semanas centrado en RAG.
El objetivo: diseñar y construir soluciones creativas que combinen modelos de lenguaje y búsqueda aumentada.
📎 Más información y bases del evento: aka.ms/raghack

Conclusión

RAG no es solo una mejora técnica: es un cambio en cómo interactuamos con la inteligencia artificial. Al combinar el poder de los modelos de lenguaje con datos relevantes y actualizados, se logra un salto cualitativo en precisión, personalización y utilidad de las respuestas.

Si estás desarrollando soluciones con IA, integrar RAG puede ser la diferencia entre un asistente genérico y uno verdaderamente útil para tu contexto.

Ver la sesión completa en YouTube

🔗 Conectemos en LinkedIn: Pablito Piova - LinkedIn

💬 Y tú, cómo aplicarías RAG en tu próximo proyecto de IA?
Cuéntalo en los comentarios y compartamos ideas para aprovechar al máximo esta tecnología.

Each session blends theory and practice, with clear explanations, live demos, and resources to help you apply what you’ve learned right away. You’ll discover how to integrate language models into your solutions, optimize performance, manage costs, implement patterns like RAG, and even develop your own personalized copilot.

Get ready for a complete journey through the tools, techniques, and best practices that are shaping the future of AI-powered development. Here’s a recap of each episode along with the links to watch them whenever you like.

📺 Episode 1 – Introduction to Azure OpenAI

In this opening episode of Season of AI for Developers, Luis Beltrán and Pabito welcome us to a five-part series designed to take developers from the basics of Azure OpenAI to building intelligent copilots. The session begins with a clear explanation of what Generative AI is, how it differs from traditional AI, and why Large Language Models (LLMs) like GPT have revolutionized human–machine interaction. They also introduce concepts like “tokens,” “probability in generation,” and the transformer architecture that powers these models.

Throughout the talk, the presenters share practical examples of how these models can generate text, images, code, and more—while debunking common misconceptions. They explain how tokenization works, how models are trained on massive datasets, and why parameters like temperature and top p are important for balancing creativity and accuracy. Viewers also learn the differences between model versions (e.g., GPT-3.5 vs. GPT-4) and how training cut-off dates affect the knowledge available in each.

Finally, we see a full demonstration in Azure OpenAI Studio: creating a resource in Azure, deploying a model, setting system messages, and using the playground for live queries. We also showcase .NET and Python integrations, including multimodal examples with image inputs and DALL·E-powered image generation. It’s a solid foundation for understanding the technical basics and setting the stage for the more advanced topics to come.

📺 Episode 2 – Considerations for Implementing Models in Azure OpenAI

In this second session of Season of AI for Developers, Dive into the practical aspects of deploying and customizing Azure OpenAI models. The episode focuses on two key approaches for enriching large language models (LLMs) with private knowledge: fine-tuning, which adapts a model to specific contexts or tasks, and Retrieval-Augmented Generation (RAG), which combines document retrieval with LLMs to deliver accurate, context-aware answers. The presenters highlight how RAG overcomes common LLM limitations—such as outdated training data or lack of internal company knowledge—by integrating both up-to-date public data and proprietary information into the response process.

The session walks through the RAG architecture, explaining how documents are ingested, chunked, and indexed in Azure AI Search, and how user queries flow through document retrieval, semantic re-ranking, and finally into a GPT model to produce grounded answers. They demonstrate hybrid search techniques (combining keyword and vector search), the role of embeddings in capturing semantic meaning, and how cosine similarity helps identify the most relevant results. Real-world demos show RAG in action with both Python and .NET, including a scenario where company policy documents are queried to answer benefit-related questions, complete with citations.

Beyond RAG, the presenters address important considerations around security, privacy, and quotas. They outline Azure’s enterprise-grade protections, including role-based access, data isolation, and responsible AI safeguards. On the performance side, they explain how token-per-minute (TPM) quotas work, the impact of exceeding limits, and strategies like request redirection, retry logic with exponential backoff, and API Management for load balancing. The episode closes with resources, GitHub repos, and best practices for scaling production-grade AI solutions in Azure.

📺 Episode 3 – What’s New from Microsoft Build: PHI-3, GPT-4o, Azure Content Safety & Azure AI Studio

In this mid-season episode of Season of AI for Developers, unpack the most significant AI announcements from Microsoft Build, focusing on PHI-3, GPT-4o, Azure Content Safety, and Azure AI Studio. They begin with PHI-3, Microsoft’s family of Small Language Models (SLMs) designed for efficiency, on-premises deployment, and cost reduction. These open-source models—available in sizes like mini, small, and medium—can run locally or in the cloud, making them ideal for scenarios with strict privacy, compliance, or offline requirements. Despite their smaller size, PHI-3 models can be fine-tuned, support prompt engineering, and even integrate with the RAG pattern for grounded responses.

The session then shifts to GPT-4o and its multimodal capabilities. Unlike traditional text-only models, GPT-4o can process both text and images in a single prompt, enabling powerful use cases like extracting structured data from receipts, analyzing contracts, describing complex images, or generating travel content directly from photos. Through live demos, they showcase GPT-4o’s ability to handle mathematical queries from diagrams, summarize documents, and even create marketing copy with a specific tone and format. They also highlight practical considerations—like the model’s faster speed and lower cost compared to earlier GPT-4 variants—and preview how audio inputs may soon extend its multimodal reach.

Finally, the presenters cover Azure AI Studio, Microsoft’s unified platform for exploring, deploying, and managing AI models. They walk through the model catalog, which includes OpenAI models, PHI-3, LLaMA, Mistral, and others, plus Hugging Face integrations. Without signing in, developers can test models and compare benchmarks; by signing in with an Azure account, they can provision deployments with either dedicated compute or cost-effective serverless hosting. The episode closes with a brief grounding demo inside Azure AI Studio using the RAG pattern—retrieving answers from internal documents with citations—and a look ahead to the next topic: Semantic Kernel.

📺 Episode 4 – Getting Started with Semantic Kernel

In this fourth episode of Season of AI for Developers, introduce Semantic Kernel—an open-source SDK from Microsoft designed to help developers integrate AI capabilities into their applications more easily. Semantic Kernel acts as a bridge between AI researchers and enterprise developers, unifying workflows regardless of programming language or AI provider. It enables you to orchestrate your own code (“native functions”) alongside AI-powered capabilities, so you can infuse existing apps with LLM-driven features without rewriting everything from scratch. The SDK supports C#, Python, and (in preview) Java, and its design allows developers to switch between providers like Azure OpenAI, OpenAI, and Hugging Face with minimal changes.

The presenters explain the core concepts: the kernel as the central orchestrator, planners to break down user requests into actionable steps, semantic functions (prompts), and native functions (existing code). They show how planners leverage function calling to decide which functions to execute and in what order. They also cover optional components like memories (for context persistence), connectors (for external data), and how Semantic Kernel’s flexibility supports various app types—from console utilities to web APIs and mobile apps. Prompt templates and variable injection are demonstrated, with the ability to store prompts in structured plugin folders using config.json and .skprompt.txt files for reusability.

In the demos, walk through building a Semantic Kernel app from scratch in C#, connecting to Azure OpenAI for chat completions, and running three types of prompts: simple requests, controlled-length outputs, and streaming responses. They showcase native plugins like a Time Plugin to fetch the current date, and a custom GitHub Plugin to query a user’s repositories—illustrating how the AI automatically decides which plugin to invoke based on the query. The session closes with a preview of image generation using DALL·E through Semantic Kernel and a look ahead to Episode 5, where they’ll dive into building full copilots with planners, memories, RAG, and unit testing.

📺 Episode 5 – Build Your Own Copilot with Semantic Kernel

In the season finale of Season of AI for Developers, we take everything covered in previous episodes and show how to combine it into a full copilot solution using Semantic Kernel. They begin by deep-diving into plugins—both built-in and custom—and explain how decorators like KernelFunction and detailed descriptions expose your application’s native functions to large language models (LLMs). This lets AI agents automatically decide which functions to call (via function calling) to answer user questions or perform multi-step tasks. Examples include simple native plugins like the Time Plugin and API integrations such as a weather service, where the AI invokes the plugin twice for two different cities and compares the results to answer the query.

From there, they introduce planners, which orchestrate multiple plugins to fulfill a user’s request. Using function calling as the primary mechanism, planners analyze all available functions, select the appropriate ones, and execute them in the correct order—passing results back into the conversation until a final response is ready. The process is illustrated step-by-step, from generating JSON schemas for each function to invoking them with arguments, chaining calls, and returning integrated responses. Developers can choose to let the AI auto-invoke functions or retain full manual control.

The session then moves to memories, showing how to integrate RAG (Retrieval-Augmented Generation) with Semantic Kernel to extend an LLM’s knowledge using embeddings and a vector database. They demonstrate storing embeddings in Azure AI Search, automatically creating indexes and vector profiles, and querying them for semantically relevant matches. In the demo, a set of text documents is indexed, and the copilot answers questions based solely on that content, maintaining chat history for context. Finally, they showcase a ready-to-run copilot reference app that combines local memory, plugins, and external data sources, serving as a starting point for integrating Semantic Kernel into web or enterprise applications. The episode closes with a comparison of Semantic Kernel and LangChain, and an invitation to the community to continue exploring real-world AI integration scenarios.

Don’t miss it! Rewatch each episode to discover how you can take your applications to the next level with Microsoft AI.

Enhance your AI skills with the Season of AI for Developers learning collection on Microsoft Learn — packed with resources, hands-on labs, and guidance to help you apply everything from the series in real-world scenarios. Explore the collection here.

For additional technical content, insights, and related community discussions, check out the Season of AI for Developers page on Microsoft Tech Community.

Stay curious, keep learning!