“Retrieval-Augmented Generation (RAG) is an AI framework that enhances the output quality of large language models (LLMs) by connecting them to external, authoritative knowledge sources before generating a response. This process allows the model to pull in relevant, up-to-date information.” – Retrieval-Augmented Generation – (RAG)
Retrieval-Augmented Generation (RAG) is an AI framework that enhances the output quality of large language models (LLMs) by connecting them to external, authoritative knowledge sources before generating a response. This process allows the model to pull in relevant, up-to-date information, addressing limitations in LLMs such as outdated training data and hallucinations1,2,3.
How RAG Works
RAG integrates two core components: a retriever and a generator. The retriever uses semantic search to fetch relevant snippets from external sources like databases, documents, or knowledge bases, often stored in vector databases. These snippets are then augmented into the LLM’s prompt, enabling the generator to produce more accurate, grounded responses1,2,4,5.
The process unfolds in stages:
- Pre-processing and indexing: Unstructured data (e.g., emails, PDFs) is converted into embeddings and indexed in a vector database1.
- Retrieval: A user query is embedded and matched against the index to retrieve pertinent information4.
- Generation: The LLM synthesises the retrieved data with its parametric knowledge to form a response3,5.
Key Benefits
- Reduces hallucinations: By grounding outputs in factual data, RAG minimises fabricated information1,3.
- Provides up-to-date information: Accesses current external sources without retraining the LLM2,4.
- Enhances domain-specific accuracy: Incorporates proprietary or specialised data, such as company policies1,5.
- Cost-effective: Avoids expensive model retraining while improving trustworthiness1,6.
Advanced Variants
- Vector-based RAG: Leverages vector databases for efficient similarity searches1.
- Knowledge graphs: Organises data by entities and relationships for human-like reasoning1.
- Ensemble RAG: Combines multiple retrievers for robust results1.
Key Theorist: Patrick Lewis
Patrick Lewis is widely recognised as a primary architect of RAG, co-authoring the seminal 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” while at Facebook AI Research (now Meta AI). This paper introduced RAG as a general-purpose technique to blend LLMs with external retrieval, demonstrating superior performance on tasks like open-domain question answering4.
Lewis’s backstory with RAG stems from his PhD at University College London (UCL), where he focused on information retrieval and neural networks. Collaborating with researchers from Meta AI, UCL, and New York University, he addressed LLMs’ gaps in handling knowledge-intensive queries by proposing RAG’s dual retriever-generator paradigm. The paper’s impact propelled RAG into mainstream AI, with Lewis noting the name stuck due to lack of better alternatives4.
Post-publication, Lewis co-founded and now leads the RAG team at Cohere, an AI startup specialising in enterprise LLMs. His work continues to evolve RAG for real-world applications, emphasising scalability and integration with agentic systems1,4. Lewis’s contributions have made RAG a cornerstone of reliable generative AI, cited across industry leaders like NVIDIA, AWS, and Salesforce1,4,5.
Tags: AI, artificial intelligence, RAG, Retrieval-Augmented Generation, hallucinations, term, LLM
References
1. https://www.salesforce.com/agentforce/what-is-rag/
2. https://www.redhat.com/en/topics/ai/what-is-retrieval-augmented-generation
3. https://en.wikipedia.org/wiki/Retrieval-augmented_generation
4. https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/
5. https://aws.amazon.com/what-is/retrieval-augmented-generation/
6. https://www.ibm.com/think/topics/retrieval-augmented-generation
7. https://www.youtube.com/watch?v=T-D1OfcDW1M
