What You Will Learn
- Python for AI Development
- LLM API Integration
- Prompt Engineering
- Embeddings & Semantic Search
- Vector Databases
- Retrieval-Augmented Generation (RAG)
- Document Processing
- LangChain & LlamaIndex
- AI Chatbots & Agents
- FastAPI Backend Development
- React.js AI Integration
- AI Deployment (Docker & Cloud)
- AI Security & Evaluation
Curriculum
1. AI & LLM Basics
Learn the fundamentals of AI, Generative AI, and Large Language Models and understand how modern LLM systems work.
- AI, ML, Deep Learning overview
- LLMs, Transformers, tokens, context
- Model working & limitations
2. Python for AI
Build the Python foundation required to develop and integrate modern AI applications.
- Python basics & OOP
- APIs, JSON, file handling
- Virtual environments & packages
3. LLM APIs & Prompting
Learn how to connect applications with LLM APIs and create effective prompts for reliable AI responses.
- OpenAI API usage
- Chat completions & streaming
- Prompt engineering techniques
- Output formatting & optimization
4. Embeddings & Vector Search
Understand how text is converted into vectors and how semantic search helps AI applications find relevant information.
- Text embeddings
- Semantic similarity
- Vector search basics
5. Vector Databases
Learn how to store, organize, and efficiently search vector data used by AI and RAG applications.
- Chroma, FAISS, Pinecone, Qdrant
- pgvector & Weaviate
- Similarity search & filtering
6. RAG (Core Concept)
Understand the complete Retrieval-Augmented Generation workflow for building AI applications that use external knowledge.
- RAG architecture
- Retrieval + generation pipeline
- Document-based AI systems
- RAG best practices
7. Data Processing
Learn how to prepare real-world documents and data for use in AI and RAG applications.
- PDF, CSV, web data extraction
- Cleaning & preprocessing
- Data ingestion pipelines
8. Chunking & Retrieval
Improve AI search and response quality by learning effective document chunking and retrieval techniques.
- Text chunking methods
- Top-K retrieval
- Hybrid search & re-ranking
9. LangChain & LlamaIndex
Build structured AI workflows and knowledge-based applications using popular LLM development frameworks.
- RAG pipelines
- Chains, retrievers, memory
- Knowledge-based AI apps
10. AI Chatbots & Agents
Create conversational AI systems and intelligent agents capable of using tools and APIs to perform tasks.
- Conversational AI
- Multi-turn chat systems
- Tool calling & AI agents
- API-based automation
11. Advanced RAG
Explore advanced retrieval strategies to improve the accuracy, relevance, and performance of RAG applications.
- Hybrid & multi-query retrieval
- Graph-based RAG
- Context optimization
12. Backend Developmen
Learn how to build secure and scalable backend APIs that power AI applications.
- FastAPI for AI apps
- REST APIs & authentication
- Streaming responses
13. Frontend Development
Build modern interfaces for AI applications and connect them with backend AI services.
- React AI apps
- Chat UI & document search
- API integration
14. Evaluation & Security
Learn how to evaluate AI responses and protect applications from common AI security risks.
- RAG evaluation metrics
- Hallucination detection
- Prompt injection protection
- AI safety practices
15. Deployment
Learn how to deploy, scale, monitor, and optimize AI applications for real-world production environments.
- Docker & cloud deployment
- Scaling AI applications
- Monitoring & optimization
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