Course Overview
- Course Title: Complete RAG Bootcamp: Build, Optimize, and Deploy AI Apps
- Instructor: Muhammad Usman Mallick (Data Science Academy)
- Target Audience:
- Developers and data scientists exploring AI application design
- Machine learning engineers building context-aware LLMs
- Tech professionals integrating retrieval-augmented AI into products
- Students/researchers studying modern AI architectures (e.g., RAG)
- Prerequisites:
- Basic Python programming skills (familiarity with syntax, libraries like
pandas,requests) - Curiosity about AI/LLMs (conceptual understanding helpful but not mandatory)
- Access to a computer with internet (for Python, Jupyter/VS Code, API installations)
- Free/trial accounts for tools (OpenAI, LangChain, ChromaDB, Streamlit—setup guided in-course)
- Basic Python programming skills (familiarity with syntax, libraries like
Curriculum Highlights
- Key Topics Covered:
- RAG architecture fundamentals and enterprise-level deployment
- Embeddings & vector databases (OpenAI, ChromaDB, Pinecone) for semantic search
- Hybrid search (keyword + vector) and multi-modal RAG (text, images, PDFs)
- Agentic RAG workflows (autonomous planning, retrieval, reasoning)
- Performance optimization (prompt tuning, top-k selection, similarity thresholds)
- Security & compliance (role-based governance for enterprise RAG)
- Real-world integrations (Slack, Power BI, Notion)
- Front-end/back-end deployment (Streamlit, FastAPI)
- Evaluation metrics (semantic similarity, precision, recall)
- Key Skills Learned:
- Design end-to-end RAG systems from scratch
- Implement LangChain, LlamaIndex, FAISS, and OpenAI API pipelines
- Build AI knowledge assistants with retrieval-augmented LLMs
- Optimize retrieval accuracy and response relevance
- Deploy production-ready RAG apps with user interfaces
- Apply RAG in industry-specific use cases (finance, healthcare, legal)
Course Format
- Duration: 6 hours on-demand video + 8 articles
- Format: Self-paced online course (mobile/TV access)
- Resources:
- Hands-on labs with Jupyter notebooks
- Downloadable code templates (LangChain, Streamlit, FastAPI)
- Quizzes/exercises for reinforcement
- Certificate of completion
Tools & Technologies
- Core Tools:
- LangChain, LlamaIndex, FAISS, OpenAI API, CLIP
- Sentence Transformers, ChromaDB, Pinecone
- Deployment & Integration:
- Streamlit, FastAPI, Pandas, Slack SDK, Power BI
- Programming: Python (LLM prompt engineering, enterprise frameworks)