Definitions
Retrieval-Augmented Generation (RAG) is an AI framework that enhances large language models (LLMs) by integrating real-time, external data retrieval with generative capabilities.
This approach addresses key limitations of traditional LLMs, such as outdated knowledge, factual inaccuracies (“hallucinations”), and lack of domain-specific expertise.
Key aspects of RAG include:
Indexing
Converts external data (documents, databases, APIs) into numerical representations (embeddings) stored in vector databases for efficient retrieval.
Retrieval
Matches user queries with relevant data snippets using mathematical vector comparisons.
Augmentation
Combines retrieved data with the original prompt using prompt engineering to guide the LLM.
Generation
Produces responses grounded in both the retrieved information and the model’s training data.
Summary
RAG enables LLMs to dynamically access up-to-date or proprietary information without costly retraining, making it particularly valuable for enterprise applications like customer service chatbots and technical support systems.
By linking responses to verifiable sources, it improves transparency and reduces misinformation risks.
