How Large Language Models (LLMs) Know Things They Were Never Taught
Web search, RAG, and the illusion of current knowledge
What You'll Learn
- Why an LLM can answer questions about events that happened after its training ended
- How web search gives a model current information without changing its weights
- How RAG retrieves relevant information from private or specialized document collections
- Why retrieved information is read during inference rather than learned by the model
- How tool use lets an LLM work with search engines, code, databases, and external APIs
- Why retrieval quality, source accuracy, and model reasoning all affect the final answer