/ Stage 5: Building With AI
Stage 5 of 7
Chat interfaces are just the beginning. This stage introduces you to RAG, agents, and the architectural patterns that let AI do more than answer questions integrating with your systems and taking actions on your behalf.
RAG (Retrieval-Augmented Generation) solves one of AI's biggest limitations: knowledge cutoffs. Instead of relying only on what the model learned during training, RAG retrieves relevant information from external sources and includes it in the prompt. Result: AI that can answer questions about your documents, your data, your company.
Clear, visual explanation of how RAG augments LLMs with real-time data retrieval. Business-friendly.
Visual architecture showing user query → retrieval → generation workflow with practical examples.
Breaks down RAG into Input & Embedding → Retrieval → Generation stages. Good for understanding the mechanics.
How RAG leverages vector databases for efficient retrieval. From a leading vector DB provider but content is broadly applicable.
Comprehensive technical overview with 2025 industry context and market data.
Answer questions about internal documents
No knowledge cutoff; retrieves fresh information
Less hallucination because answers are sourced
Keep data private while using AI capabilities
Agents are AI systems that can take actions, not just generate text. They can search the web, write and execute code, interact with APIs, and complete multi-step tasks with minimal human intervention. This is where AI moves from assistant to autonomous collaborator.
Clear distinction between AI agents and generative AI. Explains autonomous decision-making without assuming technical background.
Definition, examples, and types of agents. Good overview of the landscape.
MIT research perspective on current state and future directions of agentic AI.
Strategic perspective on competitive advantage from agentic AI adoption.
Overview of major frameworks (AutoGen, CrewAI, LangChain) with comparisons.
Search the web, read files, call APIs
Write and run code to solve problems
Break complex tasks into subtasks
Remember context across interactions
Complete tasks without constant input
You don't need to write code to build AI-powered workflows. A new generation of tools lets anyone connect AI to their apps, automate processes, and create agents through visual interfaces.
Zapier's AI Copilot, 500+ AI integrations, agents across 7,000+ apps. If you know Zapier, add AI capabilities to your existing workflows.
Official courses on creating AI teammates that monitor, decide, and act.
Learning path from automation fundamentals through building AI agents. 200,000+ learners.
Full walkthrough of creating an AI agent without code. Agent-focused platform.
Tested comparison of Lindy, StackAI, Dify, Relevance AI, Vellum, Zapier, Make.
Comprehensive comparison matrix with use cases.
As you move beyond experiments to production systems, understanding architectural patterns and build vs. buy decisions becomes critical. Where should AI live in your stack? When should you build custom vs. use existing tools?
Production-grade AI system architecture: data-first, orchestrated, performant, governed.
Andreessen Horowitz on patterns defining AI application development. Strategic perspective.
Decision framework for AI initiatives. When to build custom, when to buy.
Clear distinction: RAG changes what model knows (at query-time) vs. fine-tuning changes behavior (permanently).
Practical decision trees for choosing the right approach.
Is this a competitive advantage?
Is this standard functionality?
Need it fast?
Too sensitive for vendors?
Can you maintain long-term?
If you're technical and want to build custom AI applications, these frameworks are the most popular starting points. They handle the complexity of connecting LLMs to data sources, tools, and workflows.
Official tutorials for building RAG agents and LLM applications. Requires Python basics.
Complete guide covering models, prompts, chains, memory, and agents. Accessible introduction.
Official LlamaIndex tutorials for RAG, agents, and workflows.
5-line code example for quick data ingestion and querying. Great for quick wins.
Complete path from basics through production-ready RAG apps.
If you want AI to connect to custom tools and data sources, MCP is the emerging standard.
Design multi-agent architectures, implement evaluation frameworks, and deploy reliable AI applications at scale. For engineers, architects, and technical leaders building AI-powered products.
Multi-agent vs. single-agent architectures, network patterns, orchestration.
Decision frameworks for choosing agent architectures.
Academic survey covering advanced architectures, enhancements, and robustness frontiers.
Hands-on projects to build practical RAG skills.
Governance, monitoring, data drift, phased rollout strategies.
These exercises take you from understanding building blocks to actually building with them.
Open Claude.ai, create a new Project, add some documents, and ask questions about them. You just experienced RAG --- Claude retrieved information from your documents to answer your questions.
Sign up for Zapier (free tier). Create a Zap: trigger on new email with specific subject, use AI to summarize it, send summary to Slack/yourself. Test it. You just built an AI agent that takes autonomous action.
Sketch an AI system for a real problem: Where does data come from? What AI capability is needed? Where does output go? Identify build vs. buy decisions. This builds architectural thinking even without implementation.
How to connect AI to your data for grounded, current answers. You know why this solves the knowledge cutoff problem.
AI that takes actions, not just generates text. You grasp the difference between chat and autonomous AI.
Zapier, Make, Relevance AI --- you know the landscape and can start building workflows without coding.
Build vs. buy, RAG vs. fine-tuning, when to use what. You can participate in technical design discussions.
LangChain, LlamaIndex, and MCP for those ready to build custom applications.