Retrieval
Selecting the most relevant chunks for a query before generation.
/ quick answer
Retrieval is the R in RAG: given a query, score candidate chunks by similarity (vector, keyword, or hybrid) and return the top-k to inject into the prompt. Selecting the most relevant chunks for a query before generation.
What is Retrieval?
Retrieval is the R in RAG: given a query, score candidate chunks by similarity (vector, keyword, or hybrid) and return the top-k to inject into the prompt.
What is an example of Retrieval?
Query 'refund policy' returns the 3 highest-similarity chunks from the help-center index.
Why does Retrieval matter for AI and automation?
Selecting the most relevant chunks for a query before generation. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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Related concepts
The vocabulary this page depends on.
- →Contextual Compression
Contextual compression is a technique used to reduce the size of the input context for a Large Language Model (LLM) while retaining its most relevant information, typically by summarizing or filtering.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
- →Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
- →Embedding
A numerical vector representation of text, image or audio that captures meaning for similarity search.
Related workflows
Turn this into a repeatable process.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.
- →Lovable vs Cursor
Prompt-to-app builder vs AI-assisted code editor — which one should you reach for?