About This Program
Master Retrieval-Augmented Generation (RAG) and Vector Databases with LangChain, LlamaIndex, and modern LLMs. Learn to build and deploy Gen AI apps in 3 months with mentorship, projects, and placement support.
Course Overview
This 3-month professional program transforms developers into Generative AI specialists. Learn RAG, vector databases, and LangChain through hands-on labs and real-world projects. Deploy production-ready AI apps with placement and career support.
What You Will Learn
Hands-on Projects
Apply your learning through real-world projects that build your portfolio
Unique method to make you professionally rich
Progress through our comprehensive program which makes qualified professional to earn their awards
Bronze Award
Begin your journey with foundational skills and basic certification.
Course Curriculum
Technologies You Will Learn
Master the most in-demand tools and technologies for data analytics
Python
LangChain
LlamaIndex
Hugging Face Transformers
FastAPI, Flask, Streamlit
Vector DBs: FAISS, Pinecone, Weaviate, ChromaDB
LLMs: OpenAI GPT, LLaMA, Mistral
Docker & GitHub Actions (CI/CD)
AWS/GCP/Azure for deployment
What Our Students Say
Hear from our students who have transformed their careers
Why Choose Edjamon?
Discover the difference between Edjamon and traditional platforms
| Features & Criteria | EdJAMON | Others |
|---|---|---|
Duration | 3 Months (intensive, job-ready) | Short courses (weeks) or long self-paced with no career support |
Learning Format | Live mentorship + projects + code reviews | Self-paced videos or short guided projects |
Vector DBs | Multi-DB coverage (FAISS, Pinecone, Weaviate, Chroma) | Single DB focus (often vendor-specific) |
RAG Pipelines | Full-stack RAG pipelines with deployment & scaling | Prototype-only demos, no production readiness |
Deployment & CI/CD | Docker, GitHub Actions, Cloud deployment | Rarely included |
Security & Guardrails | Prompt injection mitigation, moderation, compliance | Covered only at a theoretical level |
Placement Support | Resume prep, mock interviews, hiring partners | No placement or limited career support |
Key Highlights
Job Roles After Program
Explore exciting career opportunities
Gen AI Developer
AI Engineer
RAG Specialist
LLM Application Engineer
AI Consultant
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Resources & References
Access curated external resources to deepen your understanding
LLMs & Generative AI
Official guide for GPT models and API usage
Pre-trained models, datasets, and community resources
Open-source LLaMA models and implementation guides
Claude AI research papers and documentation
RAG & Framework Docs
Complete guide to building LLM applications
Data indexing framework for LLM applications
Step-by-step RAG implementation
Efficient open-source LLM models
Vector Databases
Cloud-native vector database for AI applications
Facebook's efficient similarity search library
Open-source ML-first vector database
Open-source embeddings database
Best Practices & Learning
OpenAI's official prompt engineering best practices
Academic foundation for RAG architectures
Real-world RAG implementation patterns
Security and ethical considerations for AI apps
Deployment & DevOps
Containerization and deployment guide
CI/CD automation and workflow automation
Build data apps with simple Python scripts
Cloud deployment and infrastructure setup
Python & Development
Complete Python language reference
Modern Python web framework for APIs
Numerical computing with Python
Data manipulation and analysis library
Frequently Asked Questions
Find answers to common questions