RAG with LlamaIndex Course in Pune

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RAG with LlamaIndex Course Overview

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Upcoming RAG with LlamaIndex Batches

21-09-2026
Mon-FriWeekdays Regular
08:00 AM & 10:00 AM Batches(Class 1Hr - 2Hrs) / Per Session
23-09-2026
Mon - FriWeekdays Regular
06:00 PM & 08:00 PM Batches(Class 1Hr - 2Hrs) / Per Session
25-09-2026
Sat-SunWeekend Batch
09:00 AM & 01:00 PM Batches(Class 2Hr - 4Hrs) / Per Session
Can't find a batch? Pick your own schedule

RAG with LlamaIndex Course Syllabus

Learn to build intelligent Retrieval-Augmented Generation (RAG) applications using LlamaIndex and modern LLM technologies. Master data ingestion, document indexing, embeddings, vector databases, retrieval, query engines, agents, and advanced RAG workflows. Develop practical, production-oriented AI applications through hands-on projects using LlamaIndex and current 2026 best practices.

  • Introduction to Generative AI and LLMs
  • RAG concepts and architecture
  • Why RAG is required
  • RAG vs. fine-tuning
  • RAG use cases and applications
  • RAG pipeline overview
  • What is LlamaIndex?
  • LlamaIndex architecture and core components
  • LLM and embedding integrations
  • Documents, Nodes and Indexes
  • Query engines and retrievers
  • Building a basic LlamaIndex application
  • Loading PDFs, Word, CSV and web data
  • LlamaIndex data connectors
  • Document parsing and preprocessing
  • Metadata extraction
  • Handling multiple data sources
  • Preparing enterprise data for RAG
  • Understanding Documents and Nodes
  • Node parsing and chunking strategies
  • Sentence and semantic splitting
  • Chunk size and overlap optimization
  • Embedding models
  • Vector representations and similarity
  • LlamaIndex indexing architecture
  • VectorStoreIndex
  • Creating and managing indexes
  • FAISS, Chroma and other vector stores
  • Persistent storage
  • Metadata filtering and indexing optimization
  • Building LlamaIndex query engines
  • Retrievers and response synthesizers
  • Prompt templates
  • Context retrieval and generation
  • Citation and source references
  • Building a complete RAG application
  • Similarity and hybrid retrieval
  • Query transformation
  • Query rewriting
  • Sub-question querying
  • Reranking and contextual retrieval
  • Recursive and hierarchical retrieval
  • Router-based retrieval
  • Conversational RAG
  • Chat engines and memory concepts
  • LlamaIndex agents
  • Tool calling and function calling
  • Multi-step and agentic retrieval
  • Multimodal RAG concepts
  • Knowledge graph integration
  • Evaluating retrieval quality
  • Response quality and relevance
  • Hallucination detection
  • RAG evaluation frameworks
  • Prompt injection and data protection
  • Latency and cost optimization
  • Debugging and observability
  • Building production-ready RAG applications
  • API integration and application architecture
  • Deployment and containerization concepts
  • Monitoring and observability
  • Scaling RAG systems
  • Enterprise RAG architecture
  • Agentic and multimodal RAG trends
  • Capstone project implementation
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Hands On RAG with LlamaIndex Projects

Enroll in our RAG with LlamaIndex classes, where our course focuses on providing high-quality training with a strong foundation in core concepts and a practical approach. Through exposure to current industry use cases and scenarios, participants will enhance their skills and gain the ability to execute real-time projects using best practice

Intelligent PDF Knowledge Assistant

  • Upload and process multiple PDF documents
  • Create Nodes, embeddings, and vector indexes
  • Build a LlamaIndex query engine
  • Implement contextual question answering
  • Provide source and citation references

Enterprise Document Intelligence System

  • Connect multiple enterprise data sources
  • Build and manage a centralized knowledge index
  • Implement metadata filtering and advanced retrieval
  • Add conversational question answering
  • Evaluate and optimize retrieval performance

Agentic Research & Knowledge Assistant

  • Build a LlamaIndex-powered AI agent
  • Combine RAG with external tools
  • Implement multi-step and sub-question retrieval
  • Add tool calling and structured responses
  • Create a production-oriented intelligent research assistant

For Corporates

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Our Instructor
Name
Ms. Anitha
Experience
7+ Years
Specialized in
Specialized in Generative AI, LlamaIndex, RAG, Large Language Models, AI Agents, Vector Databases, Knowledge Graphs, and enterprise AI application development.
More Details
The trainer specializes in building practical Generative AI solutions using LlamaIndex, RAG architectures, LLMs, and intelligent AI agents. Training combines conceptual understanding with hands-on implementation, covering data ingestion, indexing, retrieval, evaluation, optimization, and deployment. Participants gain practical experience by developing end-to-end projects based on modern 2026 RAG and AI application development practices.

RAG with LlamaIndex Training Objectives

Our Best RAG with LlamaIndex Training aims to empower participants with comprehensive skills and practical knowledge in this field. Objectives include mastering core concepts, applying skills through real-world projects, critical thinking, and tackling professional challenges. This enhances career development and contributes to industry advancement.

GenAI and LLM professionals in India can earn approximately ₹7–12 LPA at entry level, depending on skills and employer. With experience, salaries can reach ₹14–25+ LPA, with specialized roles potentially offering higher packages. Practical RAG, LlamaIndex, Python, LLM, cloud, and deployment skills can support career opportunities in this field.

Basic to intermediate Python programming knowledge is recommended for the hands-on portions of the course. You should be familiar with functions, classes, packages, APIs, and basic application development concepts. Advanced Machine Learning knowledge is not required because the RAG concepts are introduced progressively.

The course covers LlamaIndex, LLMs, embedding models, vector databases, query engines, retrievers, and RAG evaluation techniques. It also explores advanced concepts such as hybrid retrieval, reranking, agents, tool calling, and knowledge graphs. The technology stack can be adapted to current LLM and LlamaIndex ecosystem developments during 2026.

You will build a PDF Knowledge Assistant, an Enterprise Document Intelligence System, and an Agentic Research Assistant. These projects cover ingestion, indexing, vector retrieval, conversational RAG, advanced querying, and AI agents. The projects are designed to provide practical experience with complete RAG application development.

LlamaIndex and LangChain are both frameworks used to build applications powered by LLMs and external data. LlamaIndex places strong emphasis on data ingestion, indexing, retrieval, and knowledge interfaces for RAG applications. LangChain provides a broader application orchestration approach involving chains, tools, agents, prompts, and model integrations.

Yes, LlamaIndex can be used to build enterprise knowledge assistants and other data-driven AI applications. Production implementations need appropriate access controls, data security, evaluation, monitoring, scalability, and cost management. The course introduces these considerations along with techniques for building reliable production-oriented RAG systems.

Job Assistance Program

Our Job Assistance Programme offers you special guidance through the course curriculum and helps in your interview preparation.

Specialised Curriculum
Get on-field knowledge and skills from our expert instructors.
Assessment
Upgrade your on-field skills with our assessments and track your progress in real time.
Hands-on Project
Our hands-on project help you gain experience in real-time working.
Certification Guidance
A global certificate always helps you stand out from the crowd.
Portfolio Building
Experts guide you to maximise your profile with current industry trends that employers expect.
Placment Cell
We promote your abilities and showcase your portfolio to employers.

RAG with LlamaIndex Career Opportunity

RAG with LlamaIndex is the most common programming language, and it works on all computers and mobile devices without needing to be upgraded. It is one of the highest-paying careers in the software development industry, and those with the RAG with LlamaIndex certification can earn an average of  8 LPA per year.

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Infibee’s placement guidance navigates you to your desired role in top organisations, ensuring you stand out and excel in every opportunity.

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RAG with LlamaIndex Training FAQ's

Infibee RAG with LlamaIndex Training offers wide range of services that suits for both fresher and experienced persons via both offline and online at your suitable time slots.

You need not worry about having missed a class. Our dedicated course coordinator will help them with anything and everything related to administration. The coordinator will arrange a session for the student with trainers in place of the missed one.

Yes, of course. You can contact our team at Infibee Technologies, and we will schedule a free demo or a conference call with our mentor for you.

We provide classroom, online, and self-based study material and recorded sessions for students based on their individual preferences.

Yes, all our trainers are industry professionals with extensive experience in their respective domains. They bring hands-on practical and real-world knowledge to the training sessions.

Yes, participants typically receive access to course materials, including recorded sessions, assignments, and additional resources, even after the training concludes.

We provide placement assistance to students, including resume building, interview preparation, and job placement support for a wide range of software courses.

Yes, we offer customisation of the syllabus for both individual candidates and corporate also.

Yes, we offer corporate training solutions. Companies can contact us for customised programmes tailored to their team’s needs.

Participants need a stable internet connection and a device (computer, laptop, or tablet) with the necessary software installed. Detailed technical requirements are provided upon enrollment.

In most cases, such requests can be accommodated. Participants can reach out to our support team to discuss their preferences and explore available options.

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