--- title: Improving LLM Output by Combining RAG and Fine-Tuning description: Large language models (LLM) and conversational AI have great potential to make applications easier to use. Conviva shares their experience of building a... canonical: https://daily.dev/posts/improving-llm-output-by-combining-rag-and-fine-tuning-gimehxzzh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Improving LLM Output by Combining RAG and Fine-Tuning | daily.dev og:description: Large language models (LLM) and conversational AI have great potential to make applications easier to use. Conviva shares their experience of building a... og:url: https://daily.dev/posts/improving-llm-output-by-combining-rag-and-fine-tuning-gimehxzzh og:image: https://api.daily.dev/og/posts/gimEHXzzH.png og:image:alt: Improving LLM Output by Combining RAG and Fine-Tuning og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Improving LLM Output by Combining RAG and Fine-Tuning **[The New Stack](https://daily.dev/sources/newstack)** · 8 min read · 2 upvotes · 0 comments ## Summary Large language models (LLM) and conversational AI have great potential to make applications easier to use. Conviva shares their experience of building a conversational Q&A solution using LLM, their choice of open source models, and their hybrid approach of combining fine-tuning with retrieval-augmented generation (RAG) to improve the quality of answers. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#enterprise](https://daily.dev/tags/enterprise), [#llm](https://daily.dev/tags/llm), [#conversational-ai](https://daily.dev/tags/conversational-ai) [View this post on daily.dev](https://daily.dev/posts/improving-llm-output-by-combining-rag-and-fine-tuning-gimehxzzh) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Improving LLM Output by Combining RAG and Fine-Tuning","url":"https://daily.dev/posts/improving-llm-output-by-combining-rag-and-fine-tuning-gimehxzzh","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/improving-llm-output-by-combining-rag-and-fine-tuning-gimehxzzh"},"datePublished":"2024-04-30T17:56:23.726Z","dateModified":"2024-05-09T07:52:51.999Z","description":"Large language models (LLM) and conversational AI have great potential to make applications easier to use. Conviva shares their experience of building a...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/307aedb66022420733c340916a5a5a73?_a=AQAEuiZ","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/307aedb66022420733c340916a5a5a73?_a=AQAEuiZ","isAccessibleForFree":true,"articleSection":"The New Stack","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"The New Stack","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/newstack","url":"https://daily.dev/sources/newstack"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/improving-llm-output-by-combining-rag-and-fine-tuning-gimehxzzh","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":2},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"enterprise,llm,conversational-ai","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"The New Stack","item":"https://daily.dev/sources/newstack"},{"@type":"ListItem","position":3,"name":"Improving LLM Output by Combining RAG and Fine-Tuning"}]} ```