The End-to-End Journey of RAG: From User Input to Generated Text

Retrieval-augmented generation (RAG) is a natural language processing (NLP)
technique that merges the strengths of both retrieval-based and generative AI
models. RAG AI can provide precise results by utilizing pre-existing knowledge while
also processing and integrating that knowledge to generate unique, contextually
aware responses, instructions, or explanations in a human-like manner, rather than
merely summarizing the retrieved data. Unlike generative AI, RAG is a superset that
combines the advantages of both generative and retrieval AI. Additionally, RAG differs
from cognitive AI, which emulates the functioning of the human brain to produce
results.

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