| 01What it does | Generative AICreates text, code, images or answers from a prompt, grounded in your own data when needed. | Agentic AIWorks through a multi-step task: plans, calls a tool, checks the result and repeats until the job is done. |
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| 02Core building blocks | Generative AITransformers and attention, prompt engineering, large language models, vector databases and retrieval-augmented generation (RAG). | Agentic AIAgent architectures and design patterns, tool calling, agentic RAG and multi-agent orchestration. |
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| 03Frameworks you use | Generative AIOpen-source LLMs and model variants, LLM frameworks, vector databases and fine-tuning for model adaptation. | Agentic AILangChain and LCEL, LangGraph, CrewAI, AutoGen and Phidata. |
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| 04What you build | Generative AILLM applications and RAG systems, then deploy them as generative AI applications. | Agentic AIAI agents with LangGraph and Phidata, agentic RAG pipelines and multi-agent systems with LangGraph and CrewAI. |
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| 05Running it in production | Generative AILLM caching and routing, evaluation metrics and responsible AI for LLMs. | Agentic AIAgent observability and AgentOps, CI/CD, deployment and scaling of LLM systems. |
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