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The Coatue AI report is putting AI models at the centerpiece of all modern tech stacks going forward that Application Devs will be using to build on top of. It would not be controversial to say that the Ruby ecosystem lacks in its support and adoption of AI, ML and DS libraries. If we’d like to stay relevant in the future, we need to start building the foundations now. We’ll look at what Generative AI is, what kind of applications developers in other communities are building and how Ruby can be used to build similar applications today. We’ll cover Retrieval Augmented Generation (RAG), vector embeddings and semantic search, prompt engineering, and what the state of art (SOTA) in evaluating LLM output looks like today. We will also cover AI Agents, semi-autonomous general purpose LLM-backed applications, and what they’re capable of today. We'll make a case why Ruby is a great language to build these applications because of its strengths and its incredible ecosystem. After the slides, we'll build an AI Agent in 15 min.

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In this talk titled "Building AI Agents in Ruby," presented by Andrei Bondarev at the Rocky Mountain Ruby 2024 conference, the focus is on integrating AI and machine learning capabilities into the Ruby programming language. The speaker emphasizes the need for the Ruby ecosystem to advance in AI, machine learning (ML), and data science (DS) to keep up with modern tech stacks.

Key points discussed include: 
- **Understanding Generative AI**: Bondarev introduces Generative AI and its importance for application developers, highlighting its transformational effects on development processes where tasks previously taking months can now be completed in days. 
- **AI Agents Overview**: The speaker explains what an AI agent is—a semi-autonomous system that uses language models (LLMs) to perceive environments, make decisions, and take actions to accomplish specific goals. 
- **Core Components of AI Agents**: Bondarev details the components critical to building AI agents, such as reasoning and planning modules powered by LLMs, triggers for execution, and memory management through Retrieval Augmented Generation (RAG). 
- **Demos and Use Cases**: Examples from Bondarev's consulting work are shared, demonstrating practical applications of AI agents in automating business processes, like handling invoices and customer management in an e-commerce setting. The speaker shows a demo based on a fictional e-commerce store, emphasizing how an AI agent can facilitate operations such as processing new orders and handling returns. 
- **Challenges and Considerations**: There’s an acknowledgment of challenges like AI reliability, data bias, and the complexities in integrating these agents into existing workflows. The talk also touches on hallucination in model responses and the importance of structured outputs to mitigate incorrect conclusions from the models. 
- **Ruby as a Viable Language for AI**: Bondarev advocates for Ruby, asserting its elegance and the potential for developers to leverage its capabilities for AI applications, suggesting that while there’s a perception barrier, Ruby can thrive in AI given the right frameworks and libraries.

The presentation concludes by underscoring Ruby's strengths as a language for AI applications and the need for concerted efforts to build an accessible and rich library ecosystem to facilitate that growth. Through Bondarev’s insights, developers are encouraged to explore AI integration into their Ruby projects, emphasizing that the ongoing evolution in AI capabilities makes this an opportune time for innovation in the Ruby community.

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