Generative AI

Generative AI

December 15, 2025
AI
AI, Generative AI, GenAI, LLM

Generative AI #

Generative Artificial Intelligence (GenAI) refers to a class of AI systems that can generate new content such as text, images, audio, video, or code, rather than only making predictions or classifications.

GenAI systems learn patterns and representations from large datasets and use them to produce novel outputs that resemble the data they were trained on.

How Generative AI Differs from Traditional AI #

Traditional AIGenerative AI
Predicts or classifiesGenerates new content
Task-specific modelsGeneral-purpose models
Fixed outputsOpen-ended outputs
Often rule-basedData-driven and probabilistic

Core Idea of Generative AI #

Instead of learning “what label to assign”, Generative AI learns “how data is structured” and then creates new data following that structure.

Retrieval-Augmented Generation (RAG)

AI, ML
AI, Generative AI, LLM, RAG

Retrieval-Augmented Generation (RAG) #

Retrieval-Augmented Generation (RAG) is a system design pattern that improves an LLM’s answers by:

  1. Retrieving relevant information from an external knowledge source, and then
  2. Augmenting the LLM prompt with that retrieved context before generating the final response.

RAG helps an LLM look things up first, then answer using evidence.

Why RAG is Useful #

RAG is commonly used when:

  • Your knowledge is in private documents (PDFs, policies, internal wiki)
  • You need up-to-date information (things not in the model’s training data)
  • You want fewer hallucinations by grounding answers in retrieved sources
  • You want traceability (show “where the answer came from”)

RAG does not change the model weights.
It changes what the model sees at inference time by adding retrieved context.