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Introduction to RAG for LLMs: Sparse (Lexical) RAG and Dense RAG (Semantic Vector Search)

DEV Community·Jun Bae·about 1 month ago
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#rag#how#machinelearning#ai#python#fullscreen
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Introduction LLMs store information within their own parameters. By being trained on massive datasets, the models learn this data. But what if they are asked about the information they don't know? These queries will likely result in hallucinations or entirely wrong answers. As we know, updating the models with current data is very difficult and resource-intensive. Therefore, most AI service providers do not update their models frequently. Instead, they usually leave the models as they are after release because retraining is highly inefficient. That's why all models have their knowledge cutoff dates. How, then, can they answer questions about up-to-date information? For example, "who is the president of the U.S. right now" or "Tell me today's news regarding the U.S-Iran conflict." Without external tools, they simply can't. Qwen3.5 that was released on March 2026 doesn't know the information of the last year.…

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