What is Retrieval-Augmented Generation (RAG)?
A technique where an AI model retrieves relevant information from a knowledge base before generating a response.
Why It Matters
RAG helps AI give accurate, up-to-date answers based on your own data rather than relying only on what it was trained on.
Real-World Example
A customer support chatbot that searches your help docs before answering, so it always gives accurate product information.
“Understanding terms like Retrieval-Augmented Generation (RAG) matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Vector Database
A database designed to store and quickly search through embeddings, which represent data as lists of numbers.
Semantic Search
Search that understands the meaning behind your query rather than just matching exact words.
Embeddings
A way of representing words, sentences, or other data as lists of numbers that capture their meaning.
Learn More at buildDay Melbourne
Want to understand these concepts hands-on? Join our one-day workshop and build a real web application from scratch.
Related Terms
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Embeddings
A way of representing words, sentences, or other data as lists of numbers that capture their meaning.
Vector Database
A database designed to store and quickly search through embeddings, which represent data as lists of numbers.
Semantic Search
Search that understands the meaning behind your query rather than just matching exact words.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.