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Master in
Artificial Intelligence

Introduction

The Master in Artificial Intelligence is a practical program in which the main AI models, tools, and methodologies are explored through real business case studies.

The master is designed for professionals, executives, and organizations seeking to integrate AI into their business processes and decision-making.

The goal is for participants to acquire the knowledge and skills necessary to design, build, and deploy AI solutions that generate tangible value for their companies.

Why this program?

  • Expert instructors with experience in leading AI companies and consulting firms, bringing real-world expertise in artificial intelligence and business applications.
  • All sessions are recorded so you won’t miss anything and can revisit the content anytime.
  • Access to AI tools, frameworks, and real case studies across different industries to ensure hands-on learning.
  • Certificate awarded upon successful completion of the master.

Module 1

Introduction to Modern AI Models

In this session, participants gain an overview of the main AI models available today, understanding their providers, capabilities, and limitations. The focus is on why choosing the right model is crucial for balancing performance, cost, and scalability in real business contexts. By the end, participants will be able to compare models effectively and identify which best fits a given use case.

Topics

  • Explore and compare models from Hugging Face, OpenAI, Cohere, LLaMA, Mistral, Gemini, and Claude.

  • Analyze differences in model size, speed, context window, and cost.

  • Understand the difference between generative models (for creation) and retrieval-augmented models (for knowledge-based tasks).

  • Case study: evaluating and selecting the most appropriate model for a business-specific challenge.

Module 2

Building Conversational AI

This session covers the creation of AI-powered chatbots and assistants. Participants learn how to design conversations, apply persona-based prompts, and connect to different LLM providers. The session emphasizes why conversational AI is key for customer service, automation, and business efficiency. By the end, participants will have developed a working chatbot prototype with basic interaction logic.

Topics

  • Connect to APIs from Hugging Face, Cohere, Gemini, OpenAI, and Claude.

  • Build role-based and persona-driven prompts for different chatbot personalities.

  • Implement basic chatbot logic in Python.

  • Create a simple web interface with Streamlit or Gradio.

  • Learn how to manage multi-turn conversations where the AI remembers context.

Module 3

Prompt Engineering

Participants discover how to craft effective prompts that significantly improve the quality of AI outputs. The session highlights the importance of structured prompting techniques in unlocking the full potential of LLMs and making them more reliable for business tasks. By practicing different strategies, participants learn how to refine and optimize prompts to achieve consistent, high-quality results.

Topics

  • Apply zero-shot, few-shot, and chain-of-thought prompting strategies.

  • Create prompts for role-based, task-oriented, and instruction-based interactions.

  • Evaluate prompt quality and refine for consistency.

  • Experiment with tools such as Hugging Face Inference API, OpenAI Playground, and Claude Studio.

Module 4

Retrieval-Augmented Generation (RAG)

This session introduces how to enhance AI models by connecting them with external knowledge sources. Participants learn why grounding LLMs with trusted data is essential to avoid incorrect or invented answers and to provide accurate, business-ready outputs. By the end, they will understand how to build a RAG pipeline that combines embeddings, vector databases, and LLMs for reliable question answering.

Topics

  • Generate embeddings with Hugging Face, OpenAI, Cohere, and Gemini.

  • Use vector databases like ChromaDB, Pinecone, Weaviate, Milvus, and Qdrant.

  • Combine retrievers with LLMs using LangChain.

  • Case study: build a system that answers questions from internal documentation or PDFs.

Module 5

Multi-Document Question Answering

This session expands on RAG to work with multiple documents at once. Participants learn how to organize, search, and retrieve information across large sets of files, making it easier to get precise answers from reports, research papers, or company records. By the end, they will know how to set up an AI system that can handle complex queries across large collections of documents.

Topics

  • Parse documents with Unstructured, PyMuPDF, and pdfplumber.

  • Add metadata (author, date, topic) to improve retrieval accuracy.

  • Generate embeddings with multiple providers.

  • Store and query data in vector DBs.

  • Case study: querying multiple research papers or reports with a single AI assistant.

Module 6

Fine-Tuning and Custom Models

This session explains how to adapt AI models to specific industries or company needs. Participants learn why general-purpose models are not always enough and how fine-tuning them with specialized data can greatly improve accuracy and relevance. By the end, they will be able to prepare data and run fine-tuning workflows to create AI solutions tailored to their business.

Topics

  • Prepare datasets in JSONL or CSV format.

  • Apply fine-tuning with Hugging Face PEFT (LoRA), Cohere fine-tuning, or custom Mistral/Gemini models.

  • Evaluate the tuned model and iterate improvements.

  • Case study: creating a fine-tuned model for customer support FAQs.

Module 7

AI Memory and Context

This session explores how to make AI systems remember past interactions instead of treating each question in isolation. Participants learn why memory is essential for creating more natural, human-like conversations and for building assistants that can handle long-term tasks. By the end, they will know how to combine memory with knowledge retrieval to design smarter, more personalized AI systems.

Topics

  • Implement conversation memory with LangChain Memory, Redis, or SQLite.

  • Store embeddings for long-term memory in vector DBs.

  • Combine memory systems with RAG pipelines.

  • Case study: multi-turn AI support assistant that remembers user preferences.

Module 8

Evaluation and Benchmarking

This session explains how to measure whether an AI system is performing well. Participants learn why testing accuracy, reliability, and fairness is crucial before deploying AI in real-world environments. By the end, they will know how to evaluate models with different metrics, compare providers, and run stress tests to identify potential weaknesses.

Topics

  • Apply metrics: accuracy, F1, BLEU, ROUGE.

  • Use automated evaluation with LangChain Evaluate and Hugging Face Evaluate.

  • Conduct red-teaming to test model robustness.

  • Case study: benchmarking OpenAI, Claude, and Gemini on the same task.

Module 9

Advanced Applications of AI

This session goes beyond chatbots to explore other powerful uses of AI. Participants learn how to apply AI to tasks such as summarization, translation, classification, coding, and even image generation. By the end, they will understand how to integrate different AI capabilities to solve a wide range of business challenges.

Topics

  • Build systems for summarization, translation, and classification.

  • Explore code generation with Codex, Claude Code, and Gemini Code.

  • Generate and analyze images with DALL·E, Stable Diffusion, and Gemini multimodal.

  • Case study: building a multi-document summarizer with multimodal inputs.

Module 10

AI Security and Responsible Use

This session teaches how to safely design and deploy AI systems while following ethical and legal guidelines. Participants learn why security, fairness, and responsible AI use are crucial to prevent data leaks, biased outputs, and misuse of AI. By the end, they will know how to protect AI applications, detect potential risks, and implement governance frameworks to ensure trust and compliance.

Topics

  • Identify and defend against prompt injection, adversarial attacks, and data leakage.

  • Secure AI systems with authentication, rate-limiting, and API design.

  • Apply bias detection frameworks (Fairlearn, AIF360).

  • Explore governance and compliance (GDPR, EU AI Act, ethical AI standards).

Module 11

Deploying AI in Production

This session focuses on turning AI prototypes into real-world applications. Participants learn why proper deployment, monitoring, and scalability are essential for business success. By the end, they will be able to create production-ready AI systems that are robust, maintainable, and ready to serve users at scale.

Topics

  • Build REST APIs with FastAPI.

  • Test endpoints with Postman.

  • Deploy applications to AWS, Azure, or GCP.

  • Add monitoring, logging, and scaling practices.

  • Build practical UIs with Streamlit or Gradio.

Module 12

Generative AI Pipelines

This session teaches how to build end-to-end workflows that integrate multiple AI components. Participants learn why combining data ingestion, retrieval, and generation in a single pipeline improves efficiency, consistency, and business impact. By the end, they will know how to automate complex AI tasks and create fully functional systems for practical applications.

Topics

  • Build pipelines: document ingestion → embeddings → RAG → response generation.

  • Combine multiple models (e.g., Claude for reasoning, Gemini for generation).

  • Automate processes with Python pipelines.

  • Case study: customer support automation or enterprise knowledge assistant.

Module 13

Final Project

In the final session, participants integrate all the skills and knowledge acquired throughout the master into a single, professional-grade AI system. This project emphasizes the practical application of AI in real business scenarios, showing why end-to-end experience, from model selection to deployment, is essential for delivering tangible results. By the end, participants will have built a complete AI solution, gaining confidence in designing, implementing, and managing complex AI workflows.

Topics

  • Select a real-world use case (knowledge assistant, learning helper, document automation, business process AI).

  • Combine RAG, memory, fine-tuning, multimodal inputs, and deployment.

  • Evaluate, monitor, and secure the system.

  • Deliverable: a fully working AI application with UI/API plus a professional project report.

Fees and Dates

ADMISSION PROCESS

  • The course will remain open until all spots are filled.
  • To reserve your spot, you must complete the payment through the following link.

Price: 795 €

Schedule: 17:30 to 20:30 (CET)

Timetable in Madrid:

17:30
|
20:30

If you have any questions about the course, please send us an email at academic.office@aigentity.com

Calendarios

January 2026

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June 2026

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Course duration:

January - June 2026

FREQUENTLY ASKED QUESTIONS

The master is designed for professionals from diverse industries, including consulting firms, technology companies, financial institutions, and corporates seeking to leverage AI in their business processes, decision-making, and strategic initiatives. It is particularly suited for those looking to deepen their understanding of cutting-edge AI models, tools, and applications used across global markets.

In previous editions, participants had an average of 15 years of experience, and we have welcomed students from over 20 countries.

The course is completely ONLINE. The classes are live at the scheduled time and will be recorded for you to watch again whenever you want. During the sessions, you can ask questions to the instructors who will answer them live.

Yes, the course offers opportunities to network with other participants who are open to connecting. Additionally, in some courses, you will have the chance to solve case studies in study groups, collaborating with peers from different companies and countries.

In the past, we have had participants from 17 different countries, creating a rich and diverse networking environment.

You can make the payment by bank transfer or on the website itself with a debit/credit card.

Unfortunately, we do not offer payment plans at this time.

Every student in the course has the same conditions and there are no scholarships or individual discounts. Companies that register 3 or more employees have a 10% discount by employee while companies that register 6 or more employees have a 20% discount.

Yes, if you miss any session, you will be able to watch the recorded version. All recorded sessions will remain available for 2 months after the course ends, allowing you to catch up at your convenience.

To earn the course certificate, students are required to pass a test or complete a case study. This requirement can be fulfilled even if you miss a live session, as the necessary materials and guidance will be provided.

The program runs over six months, offering an interactive and engaging learning experience throughout the program.

Yes, you will have access to all course materials for 2 months after the course concludes. This allows you to review the content and reinforce your learning at your convenience during this period.

All Excel and PDF files provided during the course are downloadable.

Yes. At the end of the program, students who have successfully completed all the course modules will receive a certificate accrediting that they have completed and passed the course successfully.

Contact us through our email: academic.office@aigentity.com

www.aigentity.com

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