Business Training Program 2026
Courses in Generative Artificial Intelligence
Includes: AI Agents • MCP Protocol • Document Intelligence • NL2SQL • Healthcare AI • Compliance (detail).
More InformationTable of Contents
TRACK 1: Managers
Non-technical training focused on strategic decision-making regarding AI
- • Level 1: Generative AI for Decision-Makers (4h)
- • Level 2: AI Strategy in the Enterprise (8h)
- • Level 3: AI Compliance and Regulation (8h)
- • Level 4: AI in Regulated Industries (8h)
TRACK 2: Technical Developers (12 Months)
Intensive 12-month program to master application development with Generative AI
- • Level 1: Generative AI Fundamentals (Months 1-2)
- • Level 2: Advanced RAG and Agents (Months 3-4)
- • Level 3: Document Intelligence and OCR (Months 5-6)
- • Level 4: Production, Security, and Evaluation (Months 7-8)
- • Level 5: Multi-Agent Systems and MCP (Months 9-10)
- • Level 6: Vertical Specialization + Final Project (Months 11-12)
VERTICAL SPECIALIZATIONS
- • Healthcare AI: Medical Documents and Diagnostics
- • Legal AI: Contracts and Compliance
- • Finance AI: Risk and Regulation
- • Data Governance AI: Power BI and Taxonomies
TRACK 1: MANAGERS AND LEADERS
Non-technical training focused on strategic decision-making on AI
Target Profile: CEOs, CFOs, COOs, Department Directors, Managers, Product Owners, Legal Counsel
Level 1: Generative AI for Decision Makers
Duration: 4 hours | Format: In-person or Virtual
Objetivo: Understand what generative AI is, its real capabilities and limitations to make informed decisions.
| Module | Topic | Content |
|---|---|---|
| 1.1 | What is Generative AI? | Definition and historical evolution. Difference with traditional AI (predictive vs generative). Types of models: LLMs (GPT, Claude, Gemini, Llama), diffusion models (DALL-E, Midjourney, Stable Diffusion), multimodal models (GPT-4o, Gemini Pro Vision). The 2022-2025 leap: from ChatGPT to autonomous agents. |
| 1.2 | Real Capabilities 2025 | Generation of text, code, images and video. Document analysis and extraction (intelligent OCR, Document Intelligence). Enterprise chatbots with tools. Autonomous and multi-agent agents. Reasoning models (o1, o3): step-by-step thinking. Coding assistants (Copilot, Cursor, Claude Code). Voice AI and conversational assistants. |
| 1.3 | Limitations and Risks | Hallucinations and confabulation: why they occur, how to mitigate them. Biases in data and outputs. Hidden costs: tokens, fine-tuning, infrastructure. Security risks: prompt injection, data leakage, jailbreaks. Vendor lock-in. Impact on workforce and labor ethics. |
| 1.4 | Use Cases by Industry | Healthcare: prescription analysis, differential diagnosis, medical records. Legal: contract review, due diligence, compliance. Finance: risk analysis, fraud detection, regulatory reporting. Retail: personalization, inventory, customer service. Manufacturing: predictive maintenance, quality. HR: screening, onboarding, knowledge base. Marketing: content, segmentation, analytics. |
| 1.5 | Live Demo + Q&A | Practical demonstration with ChatGPT, Claude, Gemini. Comparison of capabilities in real time. Enterprise tools: Microsoft Copilot, Google Duet. Demo of document processing with MiKa. Q&A session. |
TRACK 2: DEVELOPERS (TECHNICAL)
Intensive 12-month program to master application development with Generative AI
Target Profile: Developers, Software Architects, Data Scientists, MLOps Engineers
Total: 480 hours | 24 Labs | 3 Intermediate Projects | 1 Final Enterprise Project
Level 1: Generative AI Fundamentals (Months 1-2)
Duration: 80 hours | Labs: 4
| Weeks | Topic | Detailed Content | Lab/Practice |
|---|---|---|---|
| 1-2 | LLM Fundamentals | Transformer architecture in depth. Self-attention, multi-head attention, positional encoding. Tokenization: BPE, SentencePiece, tiktoken. Context window and limitations. Pre-training vs fine-tuning. Model families: GPT, Claude, Llama, Mistral, Gemini. | Lab 1: Exploration of tokenizers and context windows |
| 3-4 | LLM APIs | OpenAI API: Chat Completions, Assistants, Function Calling. Anthropic Claude API: Messages, Tools, Vision. Google Gemini API. Parameters: temperature, top_p, max_tokens, stop sequences. Streaming responses. Structured outputs and JSON mode. Error handling and rate limits. | Lab 2: Multi-model chatbot with fallback |
| 5-6 | Embeddings and Similarity | Semantic vectors: what they represent and how they work. Embedding models: OpenAI ada-002, Cohere, BGE, E5. Cosine, euclidean, dot product similarity. Clustering and classification with embeddings. Visualization of vector spaces. Sentence transformers and local models. | Lab 3: Semantic search system |
| 7-8 | Vector Databases | Vector database architecture. Comparison: Pinecone, Weaviate, Chroma, Qdrant, Milvus, pgvector. Indexing: HNSW, IVF, PQ. Queries: metadata filtering, hybrid search. Namespaces and multi-tenancy. Scalability and performance tuning. When to use each option. | Lab 4: Knowledge base with Weaviate |
Ready to Start Your AI Journey?
Contact us for more information and program customization
What Includes:
- • 24/7 access to the learning platform
- • Downloadable materials and resources
- • Verifiable certificate of completion
- • Access to the student community and networking opportunities
- • Email and Discord support throughout the program
- • Technical Track: API credits included (OpenAI, Anthropic, Pinecone, Weaviate)
- • Technical Track: Access to GPU-equipped labs
Special Discounts:
- • Early Bird: 20% off until March 2026
- • Group Registration (3+): 15% off per person
- • Students and Academics: 25% off