Agentic RAG Fundamentals: Build AI Agents with LangGraph — PickAClass
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★ 4.0 (1) ⏱ 3h 📚 30 lessons 🎧 Audio version

Agentic RAG Fundamentals: Build AI Agents with LangGraph

A beginner-friendly guide to creating self-correcting AI retrieval agents using LangGraph and modern vector databases.

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About this course

As artificial intelligence evolves, standard retrieval models are no longer enough. Modern applications require agentic workflows—systems that can evaluate, route, and self-correct their own answers for maximum accuracy. This course provides a clear, text-based introduction to building Agentic RAG (Retrieval-Augmented Generation) systems. You will start with the fundamental concepts of language models and vector databases before moving into the architecture of self-correcting agents. By reading through practical explanations and code snippets, you will understand how to orchestrate complex AI workflows using LangGraph. What you'll learn: - Understand the foundational concepts of RAG, vector databases, and prompt engineering. - Learn how agentic workflows differ from standard linear AI generation. - Build self-correcting retrieval loops that evaluate and refine their own outputs. - Apply LangGraph to orchestrate stateful, multi-step AI processes. - Practice designing logic flows for document retrieval, grading, and generation. The material is structured to build your knowledge step-by-step. We begin with core AI terminology and basic retrieval patterns, gradually introducing state management and self-reflection loops. This course is designed for beginners and developers new to AI agents—no prior experience with LangGraph or advanced machine learning is required. Start reading today to unlock the next level of intelligent application development.

Course contents

6 modules · 30 lessons · 3h

1 Foundations of RAG
Master the core components of Retrieval-Augmented Generation. 5 lessons · 30 min
  1. What is RAG?
    Introduction to RAG terminology and why it solves LLM knowledge cutoffs.    6 min
  2. Vector Databases Explained
    How AI stores and searches for information using mathematical vectors.    6 min
  3. Prompt Engineering Basics
    Crafting effective instructions to guide LLM behavior in retrieval tasks.    6 min
  4. Standard Retrieval Patterns
    Understanding the linear 'Retrieve -> Augment -> Generate' workflow.    6 min
  5. The Limits of Linear RAG
    Identifying why standard RAG fails when retrieval is irrelevant or noisy.    6 min
2 The Shift to Agentic Workflows
Understand how agents differ from static pipelines. 5 lessons · 30 min
  1. Linear vs. Agentic Workflows
    Comparing static chains with dynamic, decision-making agent systems.    6 min
  2. Intro to LangGraph
    Why LangGraph is the tool of choice for building complex AI agents.    6 min
  3. Understanding State
    The concept of an agent's memory and how it tracks progress through a task.    6 min
  4. Nodes: The Action Steps
    Defining individual units of work within an agentic graph.    6 min
  5. Edges: The Decision Paths
    Connecting nodes to create a flow of information and control.    6 min
3 Designing Logic Flows
Build the individual components of an agentic RAG system. 6 lessons · 36 min
  1. Document Retrieval Logic
    Designing a node that fetches relevant context from a vector store.    6 min
  2. The Generation Node
    Creating a node that synthesizes an answer based on retrieved context.    6 min
  3. The Concept of Grading
    Why we need to evaluate the quality of retrieved documents before using them.    6 min
  4. Building a Document Grader
    Implementing logic to score document relevance to the user query.    6 min
  5. Conditional Routing
    Using 'if/else' logic in LangGraph to decide the next step in the flow.    6 min
  6. Handling Low-Quality Results
    What to do when the retriever returns nothing useful.    6 min
4 The Self-Correction Loop
Implement loops that allow the agent to fix its own mistakes. 6 lessons · 36 min
  1. What is Self-Correction?
    The theory of feedback loops where an agent critiques its own output.    6 min
  2. Detecting Hallucinations
    Logic flows to check if a generated answer is actually supported by the facts.    6 min
  3. The Reflection Pattern
    Asking the LLM to reflect on its answer and suggest improvements.    6 min
  4. Implementing the Rewrite Node
    Creating a node that optimizes the search query for better retrieval.    6 min
  5. Looping Back to Retrieval
    Connecting the rewrite node back to the retriever for a second attempt.    6 min
  6. Ensuring Answer Relevance
    Final checks to ensure the answer actually addresses the user's question.    6 min
5 Stateful Orchestration
Manage complex, multi-step processes with persistence. 5 lessons · 30 min
  1. Advanced State Management
    Passing complex data structures through the graph without losing context.    6 min
  2. Persistence and Checkpoints
    Saving the agent's state so it can resume work or be audited later.    6 min
  3. Multi-step Process Orchestration
    Coordinating multiple nodes into a cohesive, intelligent workflow.    6 min
  4. Compiling the Graph
    Turning your nodes and edges into a functional, executable agent.    6 min
  5. Debugging LangGraph Flows
    Tools and techniques to visualize and fix issues in your agent's logic.    6 min
6 Final Project & Integration
Combine everything into a fully functional Agentic RAG system. 3 lessons · 18 min
  1. Designing the Full Workflow
    Mapping out the complete logic from query to self-corrected answer.    6 min
  2. Final Project: The Self-Correcting Assistant
    Step-by-step assembly of the Capstone agent.    6 min
  3. Course Wrap-up & Future Paths
    Summary of skills learned and where to go next in the world of AI agents.    6 min
Final project

The Reliable Researcher Agent

You will build a LangGraph-based agent that takes a user query, retrieves documents, grades them, and if they are irrelevant, rewrites the query to try again. Finally, it checks its own answer for hallucinations before presenting it.

What you'll get

  • 📜 Certificate of completion
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  • 💬 Personal AI tutor
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  • 🎧 Audio version included
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  • ♾️ Lifetime access
    Come back anytime, no expiry
  • 📱 Phone or computer
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  • 💸 14-day refund
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  • ⚡ Short & focused
    3h of practical content

Certificate of completion

Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.

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has successfully demonstrated mastery of
Agentic RAG Fundamentals: Build AI Agents with LangGraph
Skills demonstrated
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Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
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A/B test design
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1.7 hrs
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Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
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Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

Reviews (1)

Manon Colin BE Verified learner
★ 4 · June 20, 2026

Très bonne entrée en matière pour comprendre comment un agent peut se corriger tout seul avec LangGraph. La partie sur les cycles de relecture avant de répondre m'a vraiment éclairé, moi qui débutais sur les agents de récupération. J'aurais aimé un peu plus sur le débogage des graphes, mais je recommande sans hésiter.

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