Advanced AI Agents & Prompt Engineering
Learn to build multi-agent systems, RAG applications, and LLM orchestration tools using LangChain and Python.
What you will learn
Master the mechanics of prompt engineering, few-shot learning, and Chain of Thought.
Build Retrieval-Augmented Generation (RAG) pipelines with Pinecone and LangChain.
Create autonomous agents that use custom tools (web search, databases, APIs).
Develop stateful multi-agent collaboration graphs using LangGraph.
Optimize cost, latency, and token consumption of LLM APIs.
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Program Overview
AI is moving from chatbots to autonomous agents. In this comprehensive developer guide, you will master the architecture of LLM-powered agents. You will build systems that can reason, write plans, search the web, execute code, and collaborate in teams. We cover vector databases, semantic search, function calling, stateful agents (using LangGraph), and deploying LLMs to production.
Curriculum Syllabus
50 lecturesRequirements
- Intermediate Python programming skills.
- Basic understanding of API concepts.
Who is this for?
Professionals aiming to transition fields, current engineers searching for deep technical specialization, and creatives ready to build commercially viable portfolios.
Taught by Dr. Elena Rostova
AI Research Scientist
Dr. Elena Rostova is a computer scientist specializing in NLP and autonomous architectures. Formerly at OpenAI, she now researches conversational agents and cognitive LLM structures.
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