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Retrieval-Augmented Generation (RAG) — From Fundamentals to Production-Ready Agentic RAG Systems

Capstone Overview and Architecture Design

Plan the capstone project architecture: multi-document ingestion, hybrid retrieval, agentic LangGraph workflow, RAGAS evaluation, and LangSmith monitoring.

Learning Goals

  • Plan a production-ready RAG system architecture
  • Design the component interactions for the capstone project

Capstone Overview and Architecture Design

Welcome to the final module of the RAG Engineering course. In this capstone project, you will move beyond isolated techniques and build a unified, production-grade Autonomous Knowledge Agent. This project will require you to integrate everything we've studied: advanced document ingestion, multi-stage retrieval, agentic reasoning loops, and automated evaluation.

In this first section, we will define the project requirements and architect the system to handle complex, real-world knowledge queries.

Learning Goals

  • Define the technical requirements for a production-ready RAG agent.
  • Architect a multi-stage pipeline combining Vector and Web retrieval.
  • Map the data flow from raw ingestion to evaluated generation.

Core Concepts

1. The Project Goal

We are building the "Global Tech Support Agent".

  • The Data: A mix of internal technical manuals (PDFs) and live web data (GitHub Issues, Documentation).
  • The Challenge: Answer complex questions that require both deep internal context and real-time external updates.

2. The Architectural Requirements

Your capstone system must implement these 4 "Production Pillars":

  1. Hybrid Ingestion: Handle both static files and dynamic web links.
  2. Adaptive Retrieval: Decide whether to use internal docs, web search, or both.
  3. Self-Correction: Grade retrieval quality and re-plan if necessary.
  4. Continuous Evaluation: Measure every response using RAGAS metrics.

Capstone Architecture Map

Project Setup and Planning

  1. 1
    Step 1

    Create a new project folder and set up your .env file with keys for OpenAI, Tavily, Cohere, and LangSmith.

  2. 2
    Step 2

    Architect your LangGraph state to track the conversation history, retrieved documents, and the current confidence score.

  3. 3
    Step 3

    Identify the 'routing' points. When should the agent pivot to the web? When should it ask for clarification?

  4. 4
    Step 4

    Enable tracing so you can visualize the graph execution in real-time during development.

Example: The "Real-World" Flow

A user asks: "How do I fix the 'Out of Bounds' error in the new v5.2 SDK?"

  1. Ingestion: The agent checks its v5.0 manual (local).
  2. Grading: The Grader realizes v5.0 docs don't mention v5.2.
  3. Pivot: The agent triggers a Tavily search for "v5.2 SDK out of bounds fix."
  4. Synthesis: The agent combines the old architectural knowledge with the new web-found fix.
  5. Output: A faithful, accurate, and up-to-date answer.

Recap

  • The capstone project synthesizes all previous modules.
  • We are building a "Hybrid Agent" (Vector + Web).
  • Production-readiness is defined by self-correction and automated evaluation.

Knowledge Check

Question 1 of 3
Q1Single choice

What is the primary challenge of the capstone project compared to previous modules?

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Capstone Overview and Architecture Design | Retrieval-Augmented Generation (RAG) — From Fundamentals to Production-Ready Agentic RAG Systems | Coursify