다음에 대한 학습 방향 Technical Professionals
Build LLM-Powered Applications & Agents for Developers
This starter learning path guides developers through building and deploying LLM-powered applications and agents using technologies such as OpenAI API, LangChain, LangGraph, Fine-Tuning, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), vector databases (Chroma, Pinecone), on-device AI applications, and cybersecurity risk management in GenAI. Learners gain hands-on experience with real-world projects, agentic workflows, prompt engineering, and securing AI systems.
Learning objectives:
- Implement LLM Applications: Build, integrate, and deploy LLM-powered applications using OpenAI API, LangChain, and LangGraph, including hands-on projects like ChatGPT clones and AI agents.
- Master Retrieval-Augmented Generation and Vector Databases: Design and optimize RAG workflows, leverage vector databases (Chroma, Pinecone), and enhance information retrieval in AI systems.
- Fine-Tune and Customize LLMs: Apply fine-tuning techniques, including LoRA, to adapt LLMs for specific tasks and create tailored AI solutions.
- Develop On-Device and Secure AI Applications: Construct on-device AI applications with modern frameworks, implement robust design patterns, and address cybersecurity risks in generative AI systems.
- Utilize Model Context Protocol and Agentic Workflows: Integrate MCP for tool interoperability, orchestrate agent-to-agent communication, and build scalable, production-ready agentic workflows.
Target audience:
This path is designed for software developers, machine learning engineers, and technical professionals seeking to build, deploy, and secure LLM-powered applications and agents. It is ideal for those with foundational programming skills who want hands-on experience with state-of-the-art GenAI technologies, vector databases, and AI security best practices.