Table of Contents
What Problem Does Contextual Retrieval Solve in RAG Systems?
Discover how contextual retrieval solves the biggest flaw in RAG systems: the loss of broader document context when large files are split into smaller chunks.
Question
What is a text embedding?
A. A summary of the main points in a document
B. A numerical representation of the meaning contained in text
C. The original text stored in a database
D. A compressed version of a text file
Answer
B. A numerical representation of the meaning contained in text
Explanation
A text embedding is a technique used in Natural Language Processing (NLP) that converts human language (like words, sentences, or entire documents) into a high-dimensional array of numbers, called a vector. This mathematical format captures the semantic meaning and context of the text, allowing computers to understand relationships between words (e.g., placing “doctor” and “physician” close together in the vector space) and perform complex tasks like semantic search and retrieval-augmented generation (RAG) rather than relying on exact keyword matches.