Information retrieval

As explained in Grokking AI Applications

The process of finding and obtaining relevant information from a data source for use in an AI application.

Retrieval logic finds the few most relevant knowledge chunks in a vector database when a user asks a question, giving an LLM extra context for its answer. section 4.1

Treat retrieval as the runtime phase of a three-step RAG flow:

• Ingest documents into a knowledge database during setup, repeating the process when the document base changes. section 4.1
• Search the vector database for the few best chunks relevant to the user’s question. section 4.1
• Pass the retrieved knowledge to answer generation, which runs alongside retrieval whenever a user asks a question. section 4.1

A question-driven search

A user asks about information contained in an uploaded document. Retrieval searches the vector database, selects the closest matching chunks, and supplies them as context for the generated response. section 4.1

Figure 4.1 A RAG-based flow includes three essential steps: document ingestion, document retrieval, and answer generation.

Trace retrieval as the middle stage connecting document ingestion with answer generation. section 4.1

How Grokking AI Applications covers it

How deep it goes

Defines retrieval within RAG and vector-database search, stopping short of mathematical proofs and model-architecture internals.

Examples

Uses a user question, a vector database, and selected document chunks as the concrete retrieval scenario.

What it assumes

Builds from a high-level mental model without requiring Python expertise, mathematical proofs, or model-architecture knowledge.

What you can do after

Apply it via a RAG flow by separating setup-time ingestion from per-question retrieval and generation.

How it connects

Connects document ingestion, vector databases, and answer generation within retrieval-augmented generation.

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Also explained in

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  Introduction to Generative AI, Second EditionRetrieval is the process of finding relevant information from an external collection in response to a query or representation.
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  Effective Conversational AIThe retrieval phase is the stage of a retrieval-augmented generation process that finds and obtains information relevant to an input.
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  MongoDB 8.0 in Action, Third EditionInformation retrieval is the process of finding relevant information from a collection of stored data in response to an information need.
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  Natural Language Processing in Action, Second EditionFull-text search retrieves documents by searching their complete textual contents rather than only structured fields.
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  Microsoft Azure in ActionRetrieval is the process of selecting relevant stored information to provide as context for a language model.
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  Elasticsearch in Action, Second EditionSearching textual content using tokenization, matching, relevance scoring, or related linguistic analysis.
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  Data Storytelling with Altair and AIInformation retrieval is the process of searching for and returning relevant information from a collection or external knowledge source.
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  Build a Website with ChatGPTSearch is the process of finding records whose data matches a user-supplied word or phrase.
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  Ensemble Methods for Machine LearningInformation retrieval is the field concerned with finding and ranking relevant information items in response to queries.
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  Building Reliable AI SystemsRetrieval is the process of finding relevant information from an external collection to supply as model context.

Compare how 11 books explain information retrieval →

Related concepts

• retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
• hybrid searchused by · Hybrid search is an information-retrieval technique that combines multiple retrieval methods or relevance signals to find or rank relevant results.
• similarity searchincludes · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
• chunkingused by · Chunking is the process of dividing content or data into smaller units for processing, retrieval, or organization.
• agentic RAGused by · Agentic RAG is a retrieval-augmented generation approach in which an agent dynamically decides how and when to retrieve information, often iterating or using tools before generating a response.

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Applications. The section links point to the parts of the book it draws on.