Developing RAG Applications with LlamaIndex and Gen AI

LlamaIndex allows for seamless integration of AI models with databases and other tools, making it easier to build advanced RAG applications. RAG models are gaining significant traction in industries like customer service, content generation, and data retrieval, offering businesses the ability to enhance decision-making processes and automate various workflows using AI.

Yes, learners will have access to community support and course resources for continued learning. Additionally, you can engage in discussion forums or seek help from course instructors and fellow learners to resolve any challenges encountered during your learning journey.

After completing this course, you will be well-equipped to pursue job roles such as AI Developer, Machine Learning Engineer, Data Scientist, and AI Researcher. You will have the practical skills needed to design, implement, and optimize AI applications, which are in high demand across industries such as tech, healthcare, finance, and more.

Projects include building a sequential query pipeline, a DAG pipeline, and developing a code checker using Streamlit. You'll also create applications like a conversational agent, semantic similarity evaluator, and document management tools. These projects will give you hands-on experience in deploying AI-driven solutions.

Yes, the course is designed to provide practical knowledge that can be directly applied in real-world projects. You will work with real-world data sources and AI applications, allowing you to integrate and deploy LlamaIndex-based RAG applications that solve complex problems.

No, this course is designed for those new to LlamaIndex as well as those with experience in AI or machine learning. It starts with an introduction to the framework and progressively dives into more advanced topics to ensure all learners can build strong foundations.

This course will equip you with in-demand skills for developing AI-powered applications. With industries increasingly adopting AI and LLMs for automation, your ability to design and implement sophisticated RAG applications will make you highly competitive in the job market, especially in AI development, machine learning, and data engineering roles.

You will gain skills in designing and implementing complex query pipelines, working with language embeddings and vector databases, integrating SQL databases, and utilizing agents and tools within the LlamaIndex framework. You'll also learn how to create and deploy Retrieval-Augmented Generation (RAG) applications and work with conversational prompts and semantic similarity evaluators.

This course focuses on building Retrieval-Augmented Generation (RAG) applications using LlamaIndex and generative AI tools. It will guide you through the integration of large language models (LLMs), prompt engineering, and database management to create AI-driven solutions for real-world problems.

This course is ideal for developers, data scientists, and AI enthusiasts who have a basic understanding of Python and are interested in exploring the applications of large language models and AI-driven tools like LlamaIndex. Familiarity with natural language processing and machine learning will be beneficial but not required.