1. Understand the architecture and purpose of LlamaIndex

2. Load and structure various data types (PDFs, APIs, SQL, Notion, etc.)

3. Create and manage vector-based, tree, and keyword indexes

4. Implement RAG pipelines for summarization, question answering, and document navigation

5. Customize query engines for advanced filtering and control

6. Use LlamaIndex with LangChain and other frameworks

7. Develop enterprise search and multi-document QA applications

8. Integrate LlamaIndex with embedding models and vector stores

9. Monitor and optimize query performance

10. Deploy LlamaIndex-powered solutions into production environments

Course Syllabus

1. Introduction to LlamaIndex and RAG

2. Installing and Setting Up LlamaIndex

3. Data Connectors: PDFs, APIs, SQL, Notion, Markdown, and Web

4. Index Types: Vector Index, Tree Index, List Index, Keyword Table

5. Query Engines and Query Modes: Natural Language, Structured, Hybrid

6. Embedding Models: OpenAI, HuggingFace, Cohere

7. Integrating with Vector Stores: FAISS, Chroma, Weaviate, Pinecone

8. Using LlamaIndex with LangChain and Streamlit

9. Building Chatbots and Search Tools with Indexed Data

10. Custom Prompts and Output Parsers

11. Real-Time Document Updates and Incremental Indexing

12. Debugging and Evaluating RAG Systems

13. Deploying RAG Applications to Production

14. Case Studies: Legal Research Bot, Enterprise Search, PDF QA Assistant

Upon successful completion of this course, you will receive the Uplatz Certificate of Mastery in LlamaIndex and RAG Systems. This industry-recognized certificate verifies your expertise in designing, developing, and deploying Retrieval-Augmented Generation pipelines using LlamaIndex.

You’ll demonstrate proficiency in indexing external data sources, connecting them with LLMs, and creating tools such as intelligent search assistants, document readers, and knowledge-aware chatbots. This certification serves as a testament to your ability to build scalable AI applications grounded in reliable data.

LlamaIndex is gaining popularity in enterprise AI workflows, especially in document-heavy industries like legal, healthcare, finance, and research. With the rising adoption of RAG systems, companies are hiring developers who can design trustworthy, verifiable AI tools.

Career roles include:

• Retrieval-Augmented Generation Engineer

• AI Search & Knowledge Engineer

• LLM Infrastructure Developer

• Data-Aware Chatbot Developer

• NLP Engineer

• Enterprise AI Architect

Professionals trained in LlamaIndex stand out in AI job markets where credibility, accuracy, and transparency are essential. The ability to connect LLMs with organizational data makes you a valuable asset in the future of AI-driven productivity.

1. What is LlamaIndex use****d for?
   LlamaIndex enables LLMs to retrieve and use external knowledge for answering questions and summarizing documents.

2. How is LlamaIndex different from LangChain?
   LlamaIndex focuses on indexing and querying data, while LangChain focuses on chaining reasoning and tools.

3. What types of data can be loaded into LlamaIndex?
   PDFs, Markdown, Notion, SQL, CSVs, APIs, web pages, and more.

4. What is a Vector Index in LlamaIndex?
   It stores embeddings of text chunks for semantic search using vector similarity.

5. What is a Tree Index used for?
   It enables hierarchical summarization and navigation of large documents.

6. Can LlamaIndex work with real-time updates?
   Yes, it supports incremental indexing and live document updates.

7. Which embedding models are supported in LlamaIndex?
   OpenAI, HuggingFace, Cohere, and others.

8. How do you integrate LlamaIndex with LangChain?
   LlamaIndex provides components that plug into LangChain’s chains and agents.

9. What is a RAG application?
   It combines an LLM with external data retrieval to generate more accurate and grounded answers.

10. Give a real-world use case of LlamaIndex.
    Creating a legal document assistant that can summarize laws and answer questions using indexed statutes.