Bridging Information Retrieval and Text Generation for Smarter AI
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Retrieval-Augmented Generation (RAG) is an NLP framework that combines information retrieval with text generation.
It allows large language models (like ChatGPT) to access external knowledge sources (e.g., Wikipedia, databases) to produce more accurate, factual, and up-to-date responses.
Developed by Facebook AI (Meta) in 2020.
Instead of relying only on what it was trained on, RAG can “look up” information before answering — like how humans quickly search Google before replying.
The model receives a user question.
A retriever model (like DPR or BM25) searches external documents for relevant info.
The retrieved passages are combined with the query.
A generator model (usually a transformer like BART or T5) uses both the query and documents to generate the final answer.
e.g., enterprise chatbots
e.g., medical, legal domains
Summarizing large documents
Improving AI search functionality
Provides more accurate and reliable responses
Enables real-time updates to the knowledge base
No need to store all data in parameters
Allows feeding specific databases for customization
RAG is a powerful hybrid NLP model that merges retrieval and generation to make AI smarter, more factual, and adaptable.
It represents the next step in language model evolution, combining contextual understanding with real-time information access.
Expected to shape future AI systems for research, enterprise, and education.
Thank you for your attention. We hope this presentation helped you understand how Retrieval-Augmented Generation is transforming modern NLP.
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