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

Hands-On: Building a Self-Correcting RAG Agent

Build a complete self-correcting RAG agent with LangGraph. Implement reflection loops, conditional routing, and multi-tool retrieval with automatic error recovery.

Learning Goals

  • Build a self-correcting RAG agent with reflection loops
  • Implement multi-tool retrieval with automatic error recovery

Hands-On: Building a Self-Correcting RAG Agent

In this final section of Module 8, we will build a production-grade Self-Correcting RAG Agent using LangGraph. This agent will combine everything we've learned: tool-calling, document grading, and self-reflection. The agent will retrieve documents, grade them for relevance, and if they aren't good enough, it will automatically perform a web search to find better information before generating the final answer.

This "Closed-Loop" architecture is the peak of modern RAG engineering.

Learning Goals

  • Assemble a multi-node LangGraph for agentic retrieval.
  • Implement conditional routing based on document quality.
  • Build a self-healing AI loop that recovers from poor retrieval.

Core Concepts

The "Self-Correction" Flow

  1. Retrieve: Call the vector store tool.
  2. Grade: A node evaluates each document.
  3. Decide:
    • If docs are relevant → Generate.
    • If docs are irrelevant → Web Search → Generate.
  4. Reflect: Check if the final answer is actually supported by the context.

Lab Architecture

Building the Self-Correcting Agent

  1. 1
    Step 1

    Track the query, the context, and the final answer:

    1class AgentState(TypedDict):
2    question: str
3    documents: list[str]
4    generation: str
  2. 2
    Step 2

    Use a structured LLM to check relevance:

    1# Node: grade_documents
2def grade_documents(state):
3    # ... (logic to grade each doc)
4    # if score < threshold: return "web_search"
5    return "generate"
  3. 3
    Step 3

    Define the logic for the edges:

    1def decide_to_generate(state):
2    if state["search_web"]:
3        return "web_search"
4    return "generate"
5
6workflow.add_conditional_edges("grade_docs", decide_to_generate)
  4. 4
    Step 4

    Input a question where the internal docs are likely outdated to see the agent 'correct' itself by using the web.

Example: The "Bulletproof" Support Bot

Imagine a customer asks about a rare edge case.

  1. Agent searches the manual → Finds nothing relevant.
  2. Grader identifies the failure.
  3. Agent pivots to Google/Tavily → Finds a community forum post with the fix.
  4. Agent generates the answer and provides the forum link as a source. The customer gets the answer they need, and the "RAG failure" is handled silently and automatically.

Common Mistakes

  • Tight Loops: If the reflection step is too picky, the agent might get stuck generating the answer forever. Always set a limit on the number of self-correction attempts.
  • Complexity Debt: This graph has many nodes. For every node, add logging (using LangSmith) so you can see exactly where the agent decided to "correct" itself.

Recap

  • Self-correction makes RAG systems resilient and professional.
  • LangGraph is the engine that allows us to build these multi-stage reasoning loops.
  • Structured grading is the key "Sensor" that triggers the corrective action.

Knowledge Check

Question 1 of 3
Q1Single choice

What is the 'Sensor' that triggers a correction in this lab's architecture?

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