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Gen AI - RAG Application Development using LlamaIndex

You will first learn the essentials of LlamaIndex and its environment setup, followed by creating your first application. The course progressively takes you through different prompt types, including conversational prompts, and introduces semantic similarity evaluators. You’ll understand the significance of language embeddings, vector databases, and how to work with a Chroma DB or an SQL database to store and retrieve data efficiently.

Further, the course will guide you in creating and optimizing query pipelines in LlamaIndex, such as sequential query pipelines and DAG (Directed Acyclic Graph) pipelines, and working with agents and tools. You will build real-world applications, including a calculator using a ReAct agent and a document agent with dynamic tools, demonstrating the versatility of LlamaIndex in various use cases.

This course is designed for developers, data scientists, and AI enthusiasts who wish to delve deeper into LlamaIndex for advanced application development. A basic understanding of Python programming and AI concepts is recommended for this intermediate-level course. By the end of the course, you’ll be able to design, build, and deploy powerful RAG-based applications tailored to complex, real-world data needs.

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What's inside

Syllabus

Introduction

In this module, we will introduce you to the foundational concepts of RAG application development with LlamaIndex, including Large Language Models (LLMs), prompts, and the setup process. You'll also gain hands-on experience by creating your first program using LlamaIndex and advanced prompt crafting techniques.

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Career center

Learners who complete Gen AI - RAG Application Development using LlamaIndex will develop knowledge and skills that may be useful to these careers:

Generative Artificial Intelligence Developer

A Generative Artificial Intelligence Developer focuses on designing, building, and deploying AI systems capable of creating new content or intelligent responses, often leveraging large language models. This role involves developing sophisticated applications that can understand and generate human-like text, code, or other data. This course is exceptionally well-suited for aspiring Generative Artificial Intelligence Developers, as it directly teaches the development of RAG applications using LlamaIndex and LLMs. You will learn to integrate various data sources, fine-tune prompts for sophisticated AI, and create optimized query pipelines, including sequential and DAG pipelines. This practical experience in building real-world applications, such as agents with dynamic tools, provides a direct path to success in designing and deploying powerful RAG-based solutions tailored to complex data needs.

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Machine Learning Engineer

As a Machine Learning Engineer, you would be responsible for building, deploying, and maintaining machine learning models and systems which solve real-world problems. This often involves designing scalable ML pipelines, ensuring data quality, and integrating models into production environments. For those aiming to become a Machine Learning Engineer, this course offers highly relevant skills in developing and deploying RAG applications using LlamaIndex and Large Language Models. It covers essential aspects like working with various data sources, semantic similarity evaluators, language embeddings, and managing data with vector and SQL databases. The emphasis on creating and optimizing query pipelines and building real-world applications directly prepares you for engineering robust and efficient AI-driven systems.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer builds and implements AI models and algorithms to create intelligent applications and systems. This role encompasses everything from understanding AI fundamentals to deploying complex AI solutions that automate tasks or provide insights. This course is an excellent starting point for an Artificial Intelligence Engineer, providing hands-on experience with cutting-edge technologies like LlamaIndex and Retrieval-Augmented Generation (RAG). You will gain proficiency in prompt engineering, integrating LLMs with diverse data sources, and optimizing query pipelines. Learning to develop practical applications, including ReAct agents and document agents, directly aligns with the responsibilities of an Artificial Intelligence Engineer focused on designing, building, and delivering powerful, AI-driven solutions.

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Software Engineer (Artificial Intelligence)

A Software Engineer Artificial Intelligence specializes in developing software systems that incorporate AI capabilities, ranging from embedded intelligence to cloud-based solutions. This requires a strong foundation in software development principles combined with expertise in AI algorithms and deployment. This course helps build a foundation in creating robust and intelligent applications. As a Software Engineer Artificial Intelligence, you will find direct value in learning how to develop RAG applications using LlamaIndex and LLMs, covering environment setup, prompt engineering, and integrating with various data sources like Chroma DB and SQL databases. The practical experience in building and deploying real-world applications with agents and tools enhances your ability to engineer sophisticated AI-driven software solutions.

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Prompt Engineer

A Prompt Engineer is an expert in designing, refining, and optimizing prompts for large language models to achieve desired outputs and behaviors. This specialized role is critical for unlocking the full potential of LLMs in various applications, requiring a deep understanding of natural language interaction and model nuances. This course is highly relevant for aspiring Prompt Engineers, as it delves deeply into prompt engineering, advanced prompt crafting techniques, and fine-tuning prompts for sophisticated AI applications. You will explore different prompt types, including conversational prompts, and learn how LlamaIndex integrates with LLMs to enhance generation quality, directly equipping you with the specialized knowledge needed to excel in this emerging and critical field.

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Natural Language Processing Engineer

A Natural Language Processing Engineer designs and implements systems that enable computers to understand, interpret, and generate human language. This involves working with various NLP models, including large language models, to develop applications like chatbots, sentiment analyzers, and information extraction tools. The course on Gen AI with LlamaIndex and RAG provides highly pertinent skills for a Natural Language Processing Engineer. You will learn about the fundamentals of LLMs, prompt engineering, and crucially, how Retrieval-Augmented Generation enhances language understanding and generation by integrating with diverse data sources and semantic similarity evaluators. This knowledge is key for building advanced NLP applications that leverage external knowledge effectively.

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Conversational Artificial Intelligence Developer

A Conversational Artificial Intelligence Developer builds interactive AI systems, such as chatbots, virtual assistants, and dialogue agents, that can engage in natural and meaningful conversations with users. This role requires expertise in natural language understanding, response generation, and managing conversational flows. This course is particularly well-suited for an aspiring Conversational Artificial Intelligence Developer. It explicitly covers conversational prompts and the creation of agents with dynamic tools, such as the ReAct agent, for building real-world applications. The training in LlamaIndex, LLMs, and integrating various data sources helps build a foundation for creating sophisticated RAG-based conversational systems that can retrieve and generate contextually relevant responses, enhancing user interaction.

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Artificial Intelligence Solutions Architect

An Artificial Intelligence Solutions Architect designs and oversees the implementation of complex AI systems, ensuring they are scalable, robust, and align with business objectives. This role requires a broad understanding of AI technologies, data strategies, and system integration. For an Artificial Intelligence Solutions Architect, this course provides detailed insights into the architecture and capabilities of modern Gen AI applications. You will understand how RAG applications leveraging LlamaIndex and LLMs integrate with diverse data sources, including vector and SQL databases, and how query pipelines (sequential and DAG) are optimized. This knowledge is essential for designing efficient, high-performing AI solutions and making informed decisions about technology stacks and deployment strategies.

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Cloud Artificial Intelligence Engineer

A Cloud Artificial Intelligence Engineer specializes in deploying, managing, and optimizing AI workloads and applications on cloud platforms. This involves leveraging cloud services for data storage, compute, and AI/ML tools, ensuring scalability, security, and cost-efficiency. This course is particularly helpful for a Cloud Artificial Intelligence Engineer. It teaches the development of RAG applications using LlamaIndex and LLMs, which are increasingly deployed in cloud environments. Understanding environment setup, integrating with various data sources (including databases), and building query pipelines provides essential context for containerizing and orchestrating these applications on platforms like AWS, Azure, or GCP, ensuring they run efficiently and reliably at scale.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer, or MLOps Engineer, focuses on streamlining the deployment, monitoring, and maintenance of machine learning models in production environments. This ensures that AI systems are reliable, scalable, and perform effectively over time. While the course primarily focuses on development, it may be useful for a Machine Learning Operations Engineer. The practical experience in building and deploying RAG-based applications with LlamaIndex, understanding environment setup, and working with query pipelines and agents, provides crucial context for managing these systems. A deep understanding of how these applications are constructed is invaluable for designing robust MLOps pipelines for Gen AI deployments.

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Applied Artificial Intelligence Scientist

An Applied Artificial Intelligence Scientist bridges the gap between AI research and practical application. This role involves experimenting with novel AI techniques, building prototypes, and evaluating their effectiveness in solving specific business problems. The course on Gen AI - RAG Application Development using LlamaIndex may be useful for an Applied Artificial Intelligence Scientist. It covers the fundamentals of LLMs, the concepts of RAG, and hands-on application development with LlamaIndex. Understanding prompt engineering, semantic similarity evaluators, and integrating data sources helps build a foundation for experimenting with and implementing advanced Gen AI solutions, enabling you to translate cutting-edge AI concepts into tangible prototypes.

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Data Scientist Artificial Intelligence

A Data Scientist Artificial Intelligence applies advanced analytical and machine learning techniques to extract insights from data and build predictive or prescriptive models. This role often involves data collection, cleaning, feature engineering, model training, and interpreting results. This course may be useful for a Data Scientist Artificial Intelligence, as it covers the foundational concepts and practical development of RAG applications using LLMs. Data scientists increasingly leverage LLMs for tasks like text analysis and information retrieval. Understanding how to integrate LlamaIndex with various data sources, utilize language embeddings, and work with vector and SQL databases for efficient data retrieval is directly applicable to enhancing data-driven insights and building more sophisticated AI models. This role typically requires an advanced degree.

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Data Engineer: Machine Learning

A Data Engineer Machine Learning specializes in building and maintaining the infrastructure and pipelines required to collect, process, and store data for machine learning models. This ensures that data is readily available, clean, and in the correct format for AI applications. The course may be useful for a Data Engineer Machine Learning. While its primary focus is on application development, it delves into integrating LlamaIndex with various data sources and working specifically with vector databases like Chroma DB or SQL databases for efficient data storage and retrieval. This understanding of data flow within RAG applications helps building robust data pipelines specifically tailored for Gen AI systems, ensuring optimal performance and scalability.

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Artificial Intelligence Product Manager

An Artificial Intelligence Product Manager defines the strategy, roadmap, and features for AI-powered products. This requires a strong understanding of AI capabilities, market needs, and technical feasibility to guide development teams and deliver successful AI solutions. This course may be helpful for an Artificial Intelligence Product Manager. While not a hands-on development role, understanding the technical intricacies of Gen AI and RAG application development using LlamaIndex and LLMs is crucial. Knowing how prompt engineering, data source integration, and query pipelines work enables you to make informed product decisions, assess technical risks, and effectively communicate with engineering teams to bring innovative AI products to market.

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Technical Trainer Artificial Intelligence

A Technical Trainer Artificial Intelligence educates developers, engineers, and other professionals on AI technologies, tools, and best practices. This role requires not only deep technical expertise but also the ability to communicate complex concepts clearly and effectively. This course may be useful for a Technical Trainer Artificial Intelligence. The detailed curriculum on Gen AI - RAG Application Development using LlamaIndex, including LLMs, prompt engineering, LlamaIndex fundamentals, vector databases, and building real-world applications, provides comprehensive knowledge. You will gain the expertise to confidently teach others how to design, build, and deploy powerful RAG-based solutions, making you a valuable resource in a rapidly evolving field.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Gen AI - RAG Application Development using LlamaIndex.

Clean Code

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Is fundamental for anyone involved in application development, regardless of experience level. It provides practical principles and patterns for writing code that is readable, maintainable, and testable. While not a textbook on a specific technology, its concepts are universally applicable and essential for building robust applications. It is widely regarded as a must-read for developers.

Gen AI - RAG Application Development using LlamaIndex

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What's inside

Syllabus

Save this course

Career center

Reading list

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