--- title: Databricks Document Intelligence: pushing the frontier... description: Databricks launched Precision Mode for its ai_extract document extraction API, combining custom fine-tuned extraction models with an agentic processing harness... canonical: https://daily.dev/posts/databricks-document-intelligence-pushing-the-frontier-for-complex-document-extraction-lcp55fadj twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Databricks Document Intelligence: pushing the frontier for complex document extraction | daily.dev og:description: Databricks launched Precision Mode for its ai_extract document extraction API, combining custom fine-tuned extraction models with an agentic processing harness... og:url: https://daily.dev/posts/databricks-document-intelligence-pushing-the-frontier-for-complex-document-extraction-lcp55fadj og:image: https://api.daily.dev/og/posts/lCP55FaDJ.png og:image:alt: Databricks Document Intelligence: pushing the frontier for complex document extraction 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. # Databricks Document Intelligence: pushing the frontier for complex document extraction **[databricks](https://daily.dev/sources/databricks)** · 4 min read · 0 upvotes · 0 comments ## Summary Databricks launched Precision Mode for its ai_extract document extraction API, combining custom fine-tuned extraction models with an agentic processing harness to handle long documents, large outputs, and complex nested schemas. Benchmarked across roughly 9,000 documents including 10-K filings, bills of lading, technical manuals, and clinical notes, Precision Mode reached 94.7% accuracy, beating the best frontier-model chunk-and-merge baseline (GPT-5.6 Sol) by seven points. Frontier models faced failure modes like chunk timeouts, truncated outputs, and schema-nonconforming merges on long documents, while Precision Mode's approach proved more robust. It's now available via the mode parameter in ai_extract or a toggle in the Information Extraction UI. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is Precision Mode in Databricks ai_extract and how accurate is it? Precision Mode is a new option in the Databricks ai_extract API that pairs custom fine-tuned extraction models with an agentic processing harness for complex document extraction. Across roughly 9,000 benchmark documents, including long filings, invoices, and technical manuals, it achieved 94.7% accuracy, beating the strongest frontier-model chunk-and-merge baseline, GPT-5.6 Sol, by seven percentage points. _daily.dev helps engineers evaluating document-extraction accuracy stay on top of new API options like this._ ### How do I enable Precision Mode when calling ai_extract in Databricks? Set the options parameter to map('mode', 'precision') when calling the ai_extract SQL function, or toggle precision mode on in the Information Extraction UI on the Agents page. This switches processing from the default mode to the agentic, fine-tuned extraction pipeline designed for long documents and complex nested schemas. _Teams wiring up document pipelines can track API changes like this one on daily.dev._ ### Why does chunk-and-merge extraction fail on long documents with frontier LLMs like GPT or Claude? Chunking a long document, extracting from each piece independently, and merging results causes operational failures including chunk timeouts, truncated outputs, and final merges that don't conform to the requested schema. These issues were observed when benchmarking leading GPT, Claude, and Gemini models with default API settings against a 2,000-page document workload. _Developers comparing extraction strategies for large documents can follow this kind of benchmarking on daily.dev._ ## Similar posts on daily.dev - [Why Your Agents Can’t Read Enterprise Documents — and How to Fix It](https://daily.dev/posts/why-your-agents-can-t-read-enterprise-documents-and-how-to-fix-it-lexemrbet) · databricks · 0 upvotes · 0 comments - [Databricks fires back at Snowflake with SQL-based AI document parsing](https://daily.dev/posts/databricks-fires-back-at-snowflake-with-sql-based-ai-document-parsing-sdjvvh8m5) · InfoWorld · 0 upvotes · 0 comments - [Unlock Your Documents with Agentic Document Extraction Inside Snowflake](https://daily.dev/posts/unlock-your-documents-with-agentic-document-extraction-inside-snowflake-xh7i98lic) · Snowflake Community · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#data-engineering](https://daily.dev/tags/data-engineering), [#databricks](https://daily.dev/tags/databricks) [View this post on daily.dev](https://daily.dev/posts/databricks-document-intelligence-pushing-the-frontier-for-complex-document-extraction-lcp55fadj) ```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":"Databricks Document Intelligence: pushing the frontier for complex document extraction","url":"https://daily.dev/posts/databricks-document-intelligence-pushing-the-frontier-for-complex-document-extraction-lcp55fadj","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/databricks-document-intelligence-pushing-the-frontier-for-complex-document-extraction-lcp55fadj"},"datePublished":"2026-08-18T21:04:34.972Z","dateModified":"2026-08-18T21:06:06.570Z","description":"Databricks launched Precision Mode for its ai_extract document extraction API, combining custom fine-tuned extraction models with an agentic processing harness...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c4f5021bbf8a9f99dc7995bc909d7a0b?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c4f5021bbf8a9f99dc7995bc909d7a0b?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"databricks","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":"databricks","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/fa7aa720f2db4d1eba826814730482c8","url":"https://daily.dev/sources/databricks"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/databricks-document-intelligence-pushing-the-frontier-for-complex-document-extraction-lcp55fadj","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,ai-agents,data-engineering,databricks","timeRequired":"PT4M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"databricks","item":"https://daily.dev/sources/databricks"},{"@type":"ListItem","position":3,"name":"Databricks Document Intelligence: pushing the frontier for complex document extraction"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/databricks-document-intelligence-pushing-the-frontier-for-complex-document-extraction-lcp55fadj#faq","mainEntity":[{"@type":"Question","name":"What is Precision Mode in Databricks ai_extract and how accurate is it?","acceptedAnswer":{"@type":"Answer","text":"Precision Mode is a new option in the Databricks ai_extract API that pairs custom fine-tuned extraction models with an agentic processing harness for complex document extraction. Across roughly 9,000 benchmark documents, including long filings, invoices, and technical manuals, it achieved 94.7% accuracy, beating the strongest frontier-model chunk-and-merge baseline, GPT-5.6 Sol, by seven percentage points. daily.dev helps engineers evaluating document-extraction accuracy stay on top of new API options like this."}},{"@type":"Question","name":"How do I enable Precision Mode when calling ai_extract in Databricks?","acceptedAnswer":{"@type":"Answer","text":"Set the options parameter to map('mode', 'precision') when calling the ai_extract SQL function, or toggle precision mode on in the Information Extraction UI on the Agents page. This switches processing from the default mode to the agentic, fine-tuned extraction pipeline designed for long documents and complex nested schemas. Teams wiring up document pipelines can track API changes like this one on daily.dev."}},{"@type":"Question","name":"Why does chunk-and-merge extraction fail on long documents with frontier LLMs like GPT or Claude?","acceptedAnswer":{"@type":"Answer","text":"Chunking a long document, extracting from each piece independently, and merging results causes operational failures including chunk timeouts, truncated outputs, and final merges that don't conform to the requested schema. These issues were observed when benchmarking leading GPT, Claude, and Gemini models with default API settings against a 2,000-page document workload. Developers comparing extraction strategies for large documents can follow this kind of benchmarking on daily.dev."}}]} ```