Developing RAG Applications with LlamaIndex and Gen AI Practice Exam
Developing RAG Applications with LlamaIndex and Gen AI Practice Exam
About Developing RAG Applications with LlamaIndex and Gen AI Exam
The Developing RAG Applications with LlamaIndex and Gen AI exam is designed to assess the skills required to build and deploy retrieval-augmented generation (RAG) applications using the LlamaIndex framework and Generative AI models. The exam tests your ability to apply concepts such as prompt engineering, vector embeddings, document processing, and integrating data sources to create advanced AI-driven applications. It also evaluates your understanding of managing large language models (LLMs), fine-tuning, and leveraging data retrieval systems within a production environment.
Skills Required
- A solid understanding of AI concepts, including language models, NLP, and machine learning, is essential.
- Proficiency in using LlamaIndex for document indexing, embedding generation, and query handling. You should be comfortable working with LlamaIndex to create and manage a document store, and implement relevant retrieval techniques.
- In-depth knowledge of Gen AI models, including prompt generation, tuning, and integration with retrieval systems.
- Understanding the RAG architecture and building applications that combine retrieval of external knowledge with generative capabilities to answer queries and generate outputs.
- Competency in vector embeddings, working with embedding models, and leveraging them for effective retrieval and language model integration.
- Skills in integrating various data sources, such as databases, APIs, or file systems, into the RAG pipeline to enhance the knowledge base of the AI system.
- Knowledge of deploying applications into production, monitoring performance, and fine-tuning models for optimized real-world application results.
Who should take the Exam?
This exam is intended for technical professionals, data scientists, AI engineers, and developers with a focus on creating intelligent systems that use large language models (LLMs) and advanced data retrieval techniques. It is suitable for those who are involved in designing AI-driven solutions, working with generative AI, or developing enterprise applications using RAG models. This includes:
- AI Engineers and Developers
- Data Scientists
- Machine Learning Engineers
- Tech Leads and Architects
Course Outline
The Developing RAG Applications with LlamaIndex and Gen AI Exam covers the following topics -
Domain 1 - Course Overview
- Introduction to the Course
- Understanding Large Language Models (LLMs)
- Overview of LlamaIndex Framework
- Fundamentals of Prompt Engineering
- Advanced Prompt Techniques
- Setting Up Your Development Environment
- First Program Using LlamaIndex
Domain 2 - Deep Dive into LlamaIndex
- Creating and Structuring Prompt Templates
- Designing Conversational Prompts
- Evaluating Semantic Similarity
- Working with Language Embeddings and Vector Databases
- Integrating Chroma DB Vector Database
- Connecting LlamaIndex with SQL Databases
- Constructing LlamaIndex Query Pipelines
- Configuring a Basic Sequential Query Pipeline
- Building a Directed Acyclic Graph (DAG) Pipeline
- Developing a Dataframe Pipeline
- Utilizing Agents and Tools for Dynamic Processing
- Implementing a ReAct Agent to Build a Calculator
- Creating a Document Agent with Dynamic Tool Generation
- Developing a Code Checker Using Streamlit UI
