CODEBRIDGELesson 1 / 3 · Start with the big picture
Where does an AI answer come from? Follow how text is generated and where search and tools can enter the process.
Watch on YouTube ↗If playback is unavailable, watch on YouTube.A model generates an answer from the context it receives. A correct answer does not prove that search ran, and adding a reference is different from training the model.
If you ask where today's event is held, what information is needed? Write down how you would distinguish a claim that AI searched from an actual check of the announcement.
AI 답변의 비밀: 토큰, 확률, 검색의 역할 15분 정리 [LLM 원리 1강]
Can you see one part of your workflow to improve? Try that first, then use a course below when you need help with implementation.
A fluent answer and a factual answer need separate checks. Start with the input, whether search or tools actually ran, and whether the answer matches its evidence. For cost and length, check the token count for the model you use rather than counting characters.
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 details