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Retrieval-augmented generation (RAG) augments the quality of large language model (LLM) responses by retrieving valuable information before submitting the prompt.
LLMs answer questions about everything from baseball to bass guitars. That range originates from pretraining on millions of diverse documents. However, generalist LLMs’ shallow understanding of many topics diminishes their business value for domain-specific tasks. Developers sometimes mitigate this challenge by giving the model additional context through retrieval-augmented generation—better known as RAG.
All articles on RAG
Retrieval-augmented generation (RAG) failure modes and how to fix them
Discover common RAG failure modes and how to fix them. Learn how to optimize retrieval-augmented generation systems for max business value.
February 5, 2025
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How a global financial services company built a specialized AI copilot accurate enough for production
Learn how Snorkel, Databricks, and AWS enabled the team to build and deploy small, specialized, and highly accurate models which met their AI production requirements and strategic goals.
September 9, 2024
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RAG: LLM performance boost with retrieval-augmented generation
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
August 15, 2024
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Long context models in the enterprise: benchmarks and beyond
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
June 6, 2024
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How to build production-grade RAG retrieval with Snorkel Flow
See a walkthrough of how Snorkel Flow users build applications with production-grade RAG retrieval components.
June 4, 2024
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The art of data development for Enterprise LLMs
Snorkel’s Paroma Varma and Google’s Ali Arsenjani discus the role of data in the development and implementation of LLMs.
April 16, 2024
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Fine-tuned representation models boost LLM systems. Here’s how
Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.
March 5, 2024
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Insurance’s GenAI revolution: a business perspective
Snorkel CEO Alex Ratner talks with QBE Ventures’ Alex Taylor about the future of AI, LLMs and multimodal models in the insurance industry.
February 6, 2024
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Building better enterprise AI: incorporating expert feedback in system development
Enterprises that aim to build valuable GenAI applications must view them from a systems-level. LLMs are just one part of an ecosystem.
January 30, 2024
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“Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit
Snorkel AI’s Jan. 25 Enterprise LLM Summit focused on one theme: AI data development drives enterprise AI success.
January 26, 2024
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Snorkel Team
Retrieval augmented generation (RAG): a conversation with its creator
Snorkel CEO Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).
January 16, 2024
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Snorkel Flow 2023.R4: enhanced UI + PDF and Databricks tools
New unified prompting UI + RAG features, PDF annotation, Databricks MLflow integration, Snorkel Flow Studio, and datasets load 2x faster!
January 9, 2024
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First cohort of Snorkel GenAI customers sees gains up to 54 points
In its first six months, Snorkel Foundry collaborated on high-value projects with notable companies and produced impressive results.
December 20, 2023
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How predictive AI + generative AI build amazing document understanding
A proof-of-concept project that combines predictive AI + generative AI to minimize LLM’s risks while keeping their advantages.
December 5, 2023
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