Learning OpenAI Codex through Projects - From Basics to Advanced Vibe Coding Using AI
AISchool
Non-Majors Welcome: Real-World Vibe Coding Projects Created Through Conversations with AI
Beginner
Business Productivity, openai, codex
Learn context engineering techniques for creating high-quality AI agents through hands-on practice.
105 learners
Level Intermediate
Course period Unlimited
High-Quality AI Agent Development Context Engineering Techniques
The Concept and Implementation Methods of Context Engineering
How to Implement High-Quality AI Agents Using LangGraph
Use Cases of AI Agents
How to Implement AI Agents Using Generative AI
Learn Context Engineering techniques for creating high-quality AI agents through hands-on practice.
How to store and retrieve information using Memory
How to Design Optimal Prompts and Effectively Connect Them to Create High-Quality AI Agents

High-quality AI agents
Those who want to create
Those who want to go beyond implementing simple AI agents and create high-quality AI agents

LangGraph implementation skills
Those who want to improve
Those who want to improve their ability to implement complex agents using LangGraph

The latest AI trends
Those who don't want to miss out
Those who don't want to miss the latest AI trends including Context Engineering
👋 This course is a course that requires prerequisite knowledge of Python, Natural Language Processing (NLP), LLM, LangChain, and LangGraph. Please make sure to take the courses below first or have equivalent knowledge before taking this course.
Large Language Models (LLM) for Everyone Part 5 - Building Your Own AI Agent with LangGraph
Q. What is Context Engineering?
Context Engineering is building a dynamic system that provides the right information and tools in the right format so that LLMs can reasonably accomplish tasks.
Q. Why is context engineering important for creating high-quality AI agents?
With the significant improvement in LLM performance, now the key to whether problems are solved or not has become a matter of how suitable context is provided to the LLM, rather than the LLM's performance itself.
Additionally, as various methodologies and tools for building AI agent systems have emerged, an environment has been created where appropriate context can be effectively delivered to LLMs through diverse methods.
Therefore, context engineering has become one of the most important skills for AI developers.
Q. Is prior knowledge required?
This [Context Engineering for Building High-Quality AI Agents] course covers project practice implementing AI agents using the LangGraph library and LLM. Therefore, the course proceeds under the assumption that you have basic knowledge of Python, natural language processing, LLM, LangChain, and LangGraph. If you lack the prerequisite knowledge, please make sure to take the prerequisite course [Large Language Models LLM for Everyone Part 5 - Building Your Own AI Agent with LangGraph] first.
Who is this course right for?
Those who want to create high-quality AI agents
Those who want to create their own AI agent with LangGraph
Those who want to conduct research related to artificial intelligence/deep learning
Someone who is preparing for an AI (Artificial Intelligence) graduate program
Need to know before starting?
Python usage experience
Experience taking the prerequisite course [Large Language Models LLM for Everyone Part 5 - Building My Own AI Agent with LangGraph]
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