Building AI Agents in Ruby - From Foundations to Implementation

Overview

Discover how to build AI agents using Ruby, exploring RAG, vector embeddings, semantic search, and prompt engineering while creating a practical agent implementation in minutes.

Syllabus

• Introduction to AI and Ruby
Overview of AI concepts and applications
Introduction to Ruby programming for AI
Setting up the Ruby development environment
• Foundations of AI Agents
Understanding AI agents and their components
Basic AI techniques in Ruby
Implementing simple decision-making agents
• Utilizing RAG (Retrieval-Augmented Generation)
Introduction to RAG and its significance in AI
Implementing RAG in Ruby
Case study: Building a simple RAG-based solution
• Vector Embeddings for AI
Concept of vector embeddings and their use in AI
Creating and using embeddings in Ruby
Practical exercises with vector embeddings
• Semantic Search Techniques
Understanding semantic search and its applications
Building a semantic search engine in Ruby
Hands-on project: Enhancing search with semantic capabilities
• Prompt Engineering
Fundamentals of prompt engineering
Crafting effective prompts in Ruby environments
Experimentation: Testing and optimizing prompts
• Practical Implementation of AI Agents
Designing a practical AI agent architecture
Building and deploying a working AI agent in Ruby
Testing and refining agent performance
• Capstone Project
Project planning and scope outline
Building a complete AI agent utilizing covered techniques
Presentation and peer feedback session
• Future Trends and Advancements
Emerging trends in AI and Ruby
Preparing for future developments in AI agent technology

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Subjects

Programming