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Building Retrieval-Augmented Systems & Knowledge Graphs

Building Retrieval-Augmented Systems & Knowledge Graphs

This course is part of Building AI Agents with LLMs, RAG, and Knowledge Graphs Specialization

Instructor: Packt - Course Instructors

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Design and implement web scraping agents using LLMs for data collection.
Apply retrieval-augmented generation techniques to reduce hallucinations and improve accuracy.
Build and integrate knowledge graphs into AI systems for advanced reasoning and retrieval.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Building AI Agents with LLMs, RAG, and Knowledge Graphs Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

This course teaches you how to enhance large language models (LLMs) by integrating retrieval-augmented generation (RAG) and structured knowledge through knowledge graphs. You'll learn how to design, optimize, and scale AI agents that combine external data sources with advanced reasoning capabilities. The practical applications of RAG and knowledge graphs are transforming the way intelligent agents operate, improving accuracy and reducing errors.

Throughout this course, you will acquire valuable skills in building and refining AI systems. We focus on practical outcomes, where you will develop the expertise to create agents that leverage web data, RAG pipelines, and knowledge graphs. These techniques will allow you to tackle real-world challenges in AI development. What sets this course apart is its unique combination of theory and real-world application. You'll work through hands-on projects that use cutting-edge technology, ensuring that you gain practical experience while reinforcing theoretical concepts. The course emphasizes scalability, security, and the implementation of best practices. This course is ideal for AI developers, data scientists, and anyone interested in the field of intelligent agent development. It is particularly suited for individuals with a background in AI, machine learning, or software development who want to specialize in advanced AI systems. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

This module explores how large language models (LLMs) can serve as the core of intelligent web scraping agents. Learners will examine the architecture of such agents, compare single-agent and multi-agent systems, and gain hands-on familiarity with key frameworks like Haystack and AutoGen. By the end, you'll understand how to leverage LLMs and supporting libraries to automate web-based information retrieval tasks.

What's included

1 video9 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

9 readingsTotal 52 minutes

Introduction7 minutes
The Brain5 minutes
The Perception6 minutes
Classifying AI Agents7 minutes
Understanding the Abilities of Single-Agent and Multiple-Agent Systems6 minutes
Exploring the Principal Libraries5 minutes
Haystack5 minutes
AutoGen4 minutes
Creating an Agent to Search the Web7 minutes

1 assignmentTotal 16 minutes

Building Intelligent Web Scraping Systems16 minutes

This module explores how to enhance language model agents using Retrieval-Augmented Generation (RAG) to minimize hallucinations and improve reliability. Learners will examine RAG components, embedding strategies, vector databases, and evaluation metrics, and compare RAG with fine-tuning approaches. Practical application is demonstrated through building a movie recommendation agent.

What's included

1 video7 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

7 readingsTotal 49 minutes

Introduction14 minutes
Retrieval, Optimization, and Augmentation7 minutes
Embedding Strategies8 minutes
Embedding Databases5 minutes
Evaluating the Output5 minutes
Comparison Between RAG and Fine-Tuning5 minutes
Using RAG to Build a Movie Recommendation Agent5 minutes

1 assignmentTotal 16 minutes

Enhancing Language Models with Retrieval-Augmented Generation16 minutes

This module explores advanced Retrieval-Augmented Generation (RAG) techniques designed to enhance information retrieval and augmentation in AI systems. Learners will examine improvements over naïve RAG, including query transformation, reranking, modular architectures, scalability, and security considerations. By the end, participants will understand how to implement and optimize sophisticated RAG pipelines for real-world applications.

What's included

1 video13 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

13 readingsTotal 78 minutes

Introduction6 minutes
Exploring the Advanced RAG Pipeline4 minutes
Hypothetical Questions and HyDE5 minutes
Query Transformation4 minutes
Query Routing4 minutes
Reranking8 minutes
Response Optimization6 minutes
Modular RAG and Its Integration with Other Systems7 minutes
Implementing an Advanced RAG Pipeline5 minutes
Understanding the Scalability and Performance of RAG8 minutes
Parallel Processing4 minutes
Security and Privacy5 minutes
Open Questions and Future Perspectives12 minutes

1 assignmentTotal 16 minutes

Advanced RAG Techniques and Information Retrieval16 minutes

This module guides learners through the process of building, cleaning, and deploying knowledge graphs, and demonstrates how to connect them to AI agents using tools like Neo4j and LangChain. Learners will explore taxonomies, ontologies, and graph-based retrieval methods, as well as the challenges and applications of integrating knowledge graphs with large language models. The module also covers advanced topics such as graph reasoning, graph neural networks, and ongoing challenges in the field.

What's included

1 video13 readings1 assignment

1 videoTotal 1 minute

Overview1 minute

13 readingsTotal 71 minutes

Introduction4 minutes
Introduction to Knowledge Graphs7 minutes
Taxonomies and Ontologies4 minutes
Knowledge Creation7 minutes
Creating a Knowledge Graph with an LLM6 minutes
Knowledge Cleaning4 minutes
Knowledge Hosting and Deployment4 minutes
Retrieving Information with a Knowledge Graph and an LLM4 minutes
Graph-Guided Retrieval7 minutes
GraphRAG Applications4 minutes
Understanding Graph Reasoning5 minutes
Graph Neural Networks8 minutes
Ongoing Challenges in Knowledge Graphs and GraphRAG7 minutes

1 assignmentTotal 16 minutes

Knowledge Graphs and AI Agent Integration16 minutes

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Instructor

Packt - Course Instructors

Packt

1,778 Courses511,404 learners

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.