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20 lines
1.2 KiB
Plaintext
20 lines
1.2 KiB
Plaintext
# Reducing Hallucination in Structured Outputs via RAG
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import {Bleed} from 'nextra-theme-docs'
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<iframe width="100%"
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height="415px"
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src="https://www.youtube.com/embed/TUL5guqZejw?si=Doc7lzyAY-SKr21L" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowFullScreen
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/>
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Researchers at ServiceNow shared a [new paper](https://arxiv.org/abs/2404.08189) where they discuss how to deploy an efficient RAG system for structured output tasks.
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The RAG system combines a small language model with a very small retriever. It shows that RAG can enable deploying powerful LLM-powered systems in limited-resource settings while mitigating issues like hallucination and increasing the reliability of outputs.
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The paper covers the very useful enterprise application of translating natural language requirements to workflows (formatted in JSON). So much productivity can come from this task but there is a lot of optimization that can be further achieved (eg., using speculative decoding or using YAML instead of JSON).
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The paper provides some great insights and practical tips on how to effectively develop RAG systems for the real world.
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