CODEBRIDGEFree · Instructor: CodeBridge
Why can the same question get different answers, and a convincing explanation be wrong? Explore models, search, probability, and tokens in three lessons, and learn what to check in an AI answer.
No signup required
![AI 답변의 비밀: 토큰, 확률, 검색의 역할 15분 정리 [LLM 원리 1강]](https://i.ytimg.com/vi/wXkkRCX_4k0/hqdefault.jpg)
▶Start learning for freeThis course introduces the concepts. Continue with a paid course below when you are ready for coding practice.
For people who have used services such as ChatGPT and want to understand how answers are generated. Start with everyday examples, without equations or coding exercises.
Lessons are ordered from oldest upload to newest, preserving the series order. You can also choose a lesson that interests you.
The roles of a model, a chat service, and search
Why answers vary and how cost and length are counted
Choose a recent AI answer. Write down the purpose of the question, the information needed, whether search ran, and how to verify the facts. No signup required.
Download the AI answer review notes ↓Once you have a direction, continue practicing with a paid CodeBridge course that matches your goal.
Build a system that answers using retrieved references. This advanced course develops architecture and practice from Classic RAG to GraphRAG and Agentic RAG.
Before you start Basic RAG and prompt concepts · Python integration with LLM APIs · Basic vector database usage
Prerequisites and practice detailsNo. Select Start learning for free and begin right away. The recommended in-depth courses are separate paid courses on Inflearn. You can take just the free course.
Your playback position, completed lessons, and notes are saved in this browser. Clearing browser data or changing devices may remove access to them. A video ending marks a lesson complete, and you can also mark it manually.
You can take this course without knowing how to code. Examples explain models, search, and tokens. The recommended paid RAG course is an advanced course requiring experience with Python, LLM APIs, and vector databases.