AI Systems Design: RAG Pipelines and LLM Architecture

About this course

Design and prototype enterprise-grade AI solutions that create real business value. In this course, you’ll learn to distinguish when to use predictive ML versus generative AI, align AI initiatives with business outcomes, and define success criteria that balance accuracy, latency, safety, and cost. You’ll compare traditional deterministic software with probabilistic AI systems to understand where AI is appropriate—and where it isn’t. You’ll diagram modern AI system architectures and evaluate build-versus-buy decisions for key components such as models, vector databases, and orchestration layers. Through hands-on work, you’ll implement data ingestion pipelines, chunking and embedding strategies, and retrieval flows, and you’ll prepare messy, unstructured enterprise data for use in AI systems. You’ll analyze orchestration patterns including tools, chains, and agents and learn when to apply each. The course culminates in building an end-to-end retrieval-augmented generation (RAG) prototype with an interactive Streamlit UI. You’ll experiment with cost–quality trade-offs, compare RAG with fine-tuning for different use cases, and use logs to iteratively test and refine your MVP. By the end, you’ll be able to demonstrate both technical viability and business feasibility for AI solutions within an enterprise context. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

Course objectives

• provide hands-on experience with AI system design
• evaluate build-versus-buy decisions for AI components
• prepare unstructured data for AI use