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Developing RAG Applications with LlamaIndex and Gen AI

Developing RAG Applications with LlamaIndex and Gen AI

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Developing RAG Applications with LlamaIndex and Gen AI

Creating RAG Applications with LlamaIndex and Generative AI focuses on developing AI-powered apps that merge information retrieval with language generation. Unlike standard AI models that can miss details or provide outdated knowledge, these applications fetch accurate data in real-time before generating answers. This combination helps industries that require correctness and clarity, such as finance, law, or healthcare.

By using LlamaIndex, developers can seamlessly connect external data sources with Gen AI models. This allows them to design applications that are smarter, more interactive, and capable of solving complex problems. From research assistants to AI-powered customer service, the use of LlamaIndex with Generative AI is shaping the future of practical and trustworthy AI applications.

Who should take the Exam?

This exam is ideal for:

  • AI/ML engineers
  • Data scientists
  • Software developers interested in AI apps
  • Cloud and backend developers
  • Entrepreneurs working on AI-driven solutions
  • Business analysts using AI for decision-making
  • Students aiming to specialize in Generative AI

Skills Required

  • Basic knowledge of Python
  • Understanding of AI and machine learning fundamentals
  • Familiarity with APIs and databases
  • Logical problem-solving ability
  • Curiosity about AI-driven business applications

Course Outline

Domain 1 - Introduction to RAG Applications

Domain 2 - Getting Started with LlamaIndex

Domain 3 - Generative AI Basics

Domain 4 - RAG with LlamaIndex and Gen AI

Domain 5 - Working with Data Sources

Domain 6 - Building AI Applications

Domain 7 - Evaluation and Optimization

Domain 8 - Advanced RAG Concepts

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Developing RAG Applications with LlamaIndex and Gen AI FAQs

Yes, Python basics are necessary for development.

Chatbots, assistants, data search tools, and customer support apps.

It helps organize and retrieve data efficiently for AI models.

 

Finance, law, healthcare, education, customer service, and research.

Yes, it helps entrepreneurs create AI-driven solutions.

Roles in AI development, data-driven startups, enterprise AI apps, and research.

Basic familiarity is useful but not advanced expertise.