Listen: Information Retrieval
The field behind search: matching a query to documents in a collection and ranking them by estimated relevance. It supplies the retrieval half of RAG and of grounding in AI answers.
Transcript
Information retrieval is the science of finding and ranking documents that answer a query. It is older than the web, but today, it is the backbone of AI search and retrieval-augmented generation.
Modern systems use two main approaches to find information. The first is lexical retrieval, which matches exact terms. It scores documents based on how often specific words appear. The second is dense retrieval, which maps queries and documents into a shared space to match meaning, even when the words are different. Because lexical search excels at finding rare names and product codes, while dense search understands concepts, production systems usually combine the two.
To evaluate these systems, engineers measure precision and recall, using metrics that reward putting the most relevant results at the very top of the list. This is achieved through a standard two-step pipeline. First, a fast, inexpensive retriever pulls hundreds of candidate documents. Then, a more powerful model, called a cross-encoder, reorders the very best results.
In modern AI answer engines, this retrieval process happens first, and the language model runs second. The retrieved passages become the grounding snippets in the model's context window. If the retriever fails to find a page, the AI can never cite it, no matter how well it was written.