Begin by establishing a reliable development environment and secure credential handling so projects are reproducible and deployment-ready. Build core intuition for large language models, comparing classical NLP with modern approaches, and learn how providers and benchmarks shape capability selection. Progress into coding interactions with multiple model families and message types while tuning parameters to balance creativity, latency, and cost. The next phase focuses on multimodal and reasoning models, prompt templates, and LLM chains. You will implement embeddings with Chroma and FAISS, construct baseline RAG pipelines, and then advance into hybrid retrieval, corrective RAG, prompt compression, caching, and multimodal workflows. Practical exercises anchor each concept with real datasets. Finally, transition from single-call tools to full agentic systems. You will design role-aligned agents, orchestrate multi-agent collaboration with crewAI and AG2, and apply the OpenAI Agents SDK and Google ADK for tracing, guardrails, tool use, and runtime control. Interoperability topics—MCP, A2A, and ACP—prepare you to connect agents to external systems safely. The journey concludes with targeted finetuning, including LoRA, enabling you to specialize models ethically and efficiently for domain-specific tasks.