Python programming for AI, data handling, NumPy, Pandas, AI-ready coding practices, API usage, scripting, environment management, and professional development workflows using Python, VS Code, and Jupyter Notebook.

Understanding Generative AI concepts, transformer architecture basics, tokenization, embeddings, model inference, temperature & sampling strategies, and LLM limitations using platforms like OpenAI, Gemini, Claude, and LLaMA-based models.

Advanced prompt engineering, system prompts, few-shot and chain-of-thought prompting, structured outputs, tool calling, prompt optimization, evaluation strategies, and cost-efficient prompt design.

Building AI applications using LangChain and LlamaIndex, chaining models, memory handling, tool integration, multi-step workflows, and AI orchestration for real-world use cases.

Designing and implementing RAG pipelines, document ingestion, chunking strategies, embedding generation, semantic search, and vector database integration using Pinecone, FAISS, ChromaDB, and Weaviate.

Designing and deploying AI agents, task automation, decision-making workflows, multi-agent collaboration, and agent frameworks like CrewAI, AutoGPT, and agent-based orchestration models.

Developing AI-powered backend services using FastAPI and REST APIs, secure API design, authentication, rate limiting, logging, and enterprise system integration.

LLM output evaluation, hallucination control, grounding strategies, prompt testing, model comparison, bias awareness, AI ethics, responsible AI usage, and cost monitoring.

Containerization using Docker, deployment on AWS and Azure, scalable AI service hosting, environment configuration, monitoring, logging, versioning AI pipelines, and production-grade AI deployment.

End-to-end AI system design, scalable GenAI architectures, enterprise AI integration patterns, performance optimization, latency reduction, AI observability, security best practices, and real-world system troubleshooting.