kodestree’s RAG Course takes you from the fundamentals of information retrieval to building complete, production-grade Retrieval-Augmented Generation systems. In this program, you’ll work with LangChain, LlamaIndex, Pinecone, FAISS, and GPT-4o to design retrieval pipelines, generate embeddings, and reduce LLM hallucinations. The program is built by AI practitioners with over 15 years of enterprise machine learning experience, giving you a curriculum grounded in real deployment challenges rather than theory alone.
Prerequisites
- Basic Python programming knowledge
- Understanding of core machine learning concepts
- Familiarity with NLP fundamentals
- Working knowledge of large language models (LLMs)
No prior experience with RAG or vector databases is required- the course builds these concepts from the ground up.
Why Learn RAG?
Large language models are powerful, but they’re frozen at their training cutoff and prone to confidently making things up when they don’t know an answer. Retrieval-Augmented Generation fixes both problems by letting a model pull real, current information from your own documents, databases, or APIs before it responds. That’s why RAG has quickly become the default architecture for enterprise chatbots, internal knowledge assistants, customer support tools, and search-driven applications. Companies building AI products need people who can design retrieval pipelines, choose the right vector database, and tune a system so it answers accurately instead of hallucinating. Learning RAG now puts you ahead of a hiring curve that’s moving fast, most Generative AI and LLM engineering roles today expect at least working knowledge of retrieval-based architectures, not just prompting.
Course Objectives
By the end of this course, you will be able to design, build, and deploy retrieval-augmented AI systems from scratch.
- Explain why RAG has become the standard architecture for grounding LLMs in real data
- Design and build a working retrieval-augmented pipeline end to end
- Select and justify the right retrieval method and vector database for a given use case
- Apply techniques that measurably reduce hallucinations in generated output
- Evaluate a RAG system using recognized quality and performance metrics
- Take a RAG application from prototype to a monitored, production deployment
What You Will Learn
This RAG training walks you through every layer of a retrieval-augmented system, from data indexing to deployment.
- How retrieval and generation work together inside a RAG architecture
- Turning raw documents into searchable vector embeddings
- Comparing vector databases and retrieval strategies for different data types
- Writing prompts and managing context windows so LLMs use retrieved data correctly
- Assembling a full pipeline with LangChain, LlamaIndex, or Haystack
- Techniques that improve accuracy: hybrid retrieval, re-ranking, multi-hop search
- Testing, monitoring, and shipping a RAG system to production
Who is This Course For?
This RAG certification course is built for professionals who want their AI applications to reason over real, current data.
- AI/ML engineers looking to specialize in retrieval-based architectures
- Data scientists expanding into generative AI and LLM applications
- Software developers building chatbots, search tools, or Q&A systems
- NLP practitioners exploring vector search and embeddings
- Product and solution architects planning enterprise AI systems
- Final-year students and freshers aiming for a career in Generative AI
Tools You Will Work With
- LangChain
- LlamaIndex
- Haystack
- Pinecone
- FAISS
- Chroma
- Qdrant
- Elasticsearch / Weaviate
- OpenAI GPT-4o and embedding APIs
- Sentence Transformers
- Streamlit and React (for front-end integration)
Skills You Will Gain
You’ll leave this training with the practical skill set needed to build and ship retrieval-augmented AI products.
- Vector embedding and indexing
- Retrieval pipeline design
- Prompt engineering for grounded outputs
- Vector database selection and management
- RAG system evaluation and benchmarking
- Production deployment and monitoring of LLM apps
- Debugging and reducing hallucinations in generative pipelines
Career Outcomes
RAG skills are among the most sought-after in generative AI hiring right now, opening doors across engineering and applied AI roles.
- RAG Engineer
- Generative AI Developer
- LLM Application Engineer
- AI/ML Engineer
- Prompt Engineer
- NLP Engineer
- AI Solutions Architect
RAG Professionals Salary
| Experience Level | India (INR) | USA (USD) |
|---|---|---|
| Entry-Level / Fresher | ₹6 LPA – ₹12 LPA | $68,500 – $90,511 |
| Mid-Level (3-6 yrs) | ₹4 LPA – ₹20 LPA | $90,511 – $105,000 |
| Senior / Lead (6+ yrs) | ₹20 LPA – ₹58 LPA | $124,500 – $265,000+ |
| RAG/LLM Specialist Premium | ₹25 LPA – ₹50 LPA | $150,000 – $204,000 |
Why Choose kodestree for This Training?
kodestree pairs practical, instructor-led RAG training with support that continues well past your last live session.
- Experienced AI Trainers
- Trained 10K+ Individuals
- Job Support with most asked RAG Interview Questions
- Life Time Access to Recorded Lectures & Study Resources
- Practical, Industry-Oriented Training
- Flexible Learning Options
- Certification-Focused Preparation
