AI (Artificial Intelligence) Introduction & Integration Basic Training

Cui îi este dedicat cursul?

● Software Developers with little to no AI experience
● Technical professionals looking to integrate AI into existing projects
● Project Managers and any other roles interested in understanding the AI capabilities and integration options,from a high-level overview

Cunoștințe și abilități inițiale

The following is a list of the minimal prerequisites required to attend the course:

• Basic programming skills, in any programming language
  • The hands-on examples will be presented in Python, Java and TypeScrip
• Basic understanding of REST APIs access
• Basic familiarity with web applications and the related concepts
• Development environment:
  • Git installed
  • One of the following IDEs installed locally:
    ■ Cursor
    ■ Windsurf
  • pgVector, preferably ran as a Docker container
    ■ We can install it during the course, if needed
     

Prezentarea cursului

The course provides essential knowledge and hands-on experience with Artificial Intelligence concepts and integration techniques. Participants will learn how to effectively leverage AI technologies to enhance their internal projects, with a focus on practical implementation.
 

Ce subiecte abordează cursul

Day 1: AI Fundamentals and Integration Basics
● Training overview and expectations setting (~10 min)
● Environment setup (~20 min)
○ API keys (ChatGPT and/or Claude) setup, for the hands-on exercises
○ Git workshop repository access, if needed
● AI and LLM essentials (~90 min)
○ The AI & ML landscape
○ Machine Learning and LLMs fundamentals
■ LLMs comparison and visual mind-map
○ Hands-on work:
■ The first LLMs prompts
■ Optional: comparing outputs from different LLMs
● Prompt Engineering fundamentals (~60 min)
○ Basic and intermediate prompting patterns
○ Context window management
● Hands-on Lab: first LLMs interactions (~80 min)
○ Building and refining prompts
○ Response and requests formats - text, Markdown, image
○ Error handling and responses validation
● AI Tools for Developers (~75 min)
○ Tools landscape overview
○ Cursor and Windsurf introduction and hands-on
○ Hands-on: using the AI-powered tools, integration patterns overview
● Day 1 Review and Q&A (~10-15 min)

Day 2: Advanced Integration and Implementation
● Vector Databases Fundamentals (~60 min)
○ Texts, tokens, chunking and context windows
○ Chunking strategies and best practices
○ Embeddings and vector search
○ When and why to use vector databases
● Hands-on work: Vector Database Setup (~30 min)
○ Running a vector database
○ Converting simple documents to embeddings
○ Building a simple semantic search, using an LLM
● Retrieval Augmented Generation (RAG) (~60 min)
○ RAG architecture and core components
○ RAG vs pure LLM access comparison
○ Implementation patterns for business use cases
● Hands-on lab: building a simple RAG system (~60 min)
○ Designing a knowledge base for internal documents
○ Architecture overview and initial integration
○ Hands-on collaborative work
● API integration patterns and MCP (~60 min)
○ Model Context Protocol (MCP) essentials
○ If needed: hands-on work - integrating MCP access in an application
○ Costs optimization strategies
● AI Agents and Assistants Overview (~30 min)
○ Differences between Agents and Assistants
○ Implementation approaches
○ Use cases and limitations
● AI Ethics & Safety Essentials (~30 min)
○ Key considerations for responsible AI use
○ Practical safeguards for business applications
● Course wrap-up and implementation roadmap (~20 min)
○ Overview of the optional Advanced Modules
○ Implementation roadmap for internal projects
○ Final Q&A and feedback