Retrieval
Why Retrieval Is Needed
LLMs are powerful, but they have a fundamental limitation: the model's knowledge has a cutoff date and blind spots.
They don't know:
- Your company's private knowledge base
- News that happened yesterday
- Specialized domain knowledge
Retrieval is the technology that solves this: find relevant materials first, then let the model generate answers based on those materials. The model is responsible for "speaking," and Retrieval is responsible for "finding."
What Is Retrieval
One-line definition: Retrieval is the process of finding the most relevant information from large amounts of data for the current question.
Analogy: Retrieval is like a librarian — when you say "I want to find books about quantum computing for beginners," they don't hand you a book to search through yourself, but directly find and hand you the most relevant ones.
Core capability:
- Not generating answers, but locating the source of answers
- Enabling the model to have evidence for answers instead of improvising
How to Do It: Retrieval Methods
Semantic Retrieval: convert both questions and documents into vectors, find the closest content based on vector similarity. "Search by meaning" rather than "by keywords." This relies on Embedding technology — converting text into numbers so computers can compare "whether meanings are close."
Keyword Retrieval: traditional search method (inverted index, BM25), suitable for exact matching — searching names, product numbers, error codes, order numbers.
Hybrid Retrieval: combines semantic and keyword retrieval simultaneously, then fuses results. This is the most stable approach in many real systems because it balances "understanding meaning" and "exact hits."
When to Use Retrieval / RAG
Scenarios suited for RAG:
- Company internal document Q&A: models don't know your private materials by default
- Real-time information Q&A: model knowledge isn't updated in real-time
- Legal, medical, financial professional Q&A: need to answer based on materials to reduce hallucination risk
Scenarios where RAG may not be needed:
- General common sense Q&A: models usually already have basic knowledge
- Simple one-time generation tasks: no need for external knowledge
Remember this: Retrieval makes models "check first, then answer" instead of "answering from memory" — it solves the problem of model knowledge boundaries.
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