LLMWhisperer – Prep complex documents ready for use in LLMs
Hey HN! Over the last few months, we’ve seen many tools here trying to tackle the problem of making complex, unstructured documents ready for LLMs. The complexity primarily includes parsing highly complex documents in terms of format, layout, design, complex tables, checkboxes, etc, with high accuracy and reliability. LLMWhisperer is our take on the problem. LLMwhisperer solves most of the document complexity with reliable accuracy. With our user-friendly playground (https://pg.llmwhisperer.unstract.com/), you can effortlessly test your document use case. No sign-up is…
LLMWhisperer – Prep complex documents ready for use in LLMs launched on April 10, 2024 with 12 votes, #1,071 of 4,347 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
LLMWhisperer converts complex, unstructured documents into formats suitable for large language models. It handles documents with intricate layouts, tables, checkboxes, and varied formatting while maintaining high accuracy. The tool is designed for developers and organizations that need to prepare documents for LLM processing. Users can test documents in the playground without signing up, and it integrates with Unstract, an open-source document processing automation platform.
written from the facts on this page · September 2026
From the sources
LLMWhisperer helps simplify complex document data, making it easier for LLMs to understand and consume. Handle complex tables and difficult layouts with ease.
LLMs are powerful, but their output is as good as the input you provide. LLMWhisperer is a technology that presents data from complex documents (different designs and formats) to LLMs in a way that they can best understand. Large Language Models do a good job of extracting complex data, especially repeating sections and line items when the layout of documents is preserved in the extracted text. LLMWhisperer’s Layout Preserving mode lets you realize maximum accuracy from LLMs. Checkboxes and radio buttons are a very common feature of documents that are used as forms. It’s important to read their values correctly in order to make sense of such documents and process them down the chain.…from llmwhisperer.unstract.com
In the maker’s words, at launch
Hey HN! Over the last few months, we’ve seen many tools here trying to tackle the problem of making complex, unstructured documents ready for LLMs. The complexity primarily includes parsing highly complex documents in terms of format, layout, design, complex tables, checkboxes, etc, with high accuracy and…
Does a similar job
all alternatives →- PTPredictive text using only 13kb of JavaScript. no LLM2024 · adamgrant.info · ▲207
I just open sourced my document/website extractor for Vision-LLMs2024 · github.com · ▲37Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…
Beyond text splitting – improved file parsing for LLMs2024 · github.com · ▲206Improved file parsing for LLM’s. Contribute to Filimoa/open-parse development by creating an account on GitHub.
LLM Scraper – turn any webpage into structured data2024 · github.com · ▲88Turn any webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.
Robust LLM extractor for websites in TypeScriptMar 2026 · github.com · ▲72We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
PDF to MD by LLMs – Extract Text/Tables/Image Descriptives by GPT4o2024 · github.com · ▲191I've developed a Python API service that uses GPT-4o for OCR on PDFs. It features parallel processing and batch handling for improved performance. Not only does it convert PDF to markdown, but it also describes the images within the PDF using captions like `[Image: This picture shows 4 people waving]`. In testing with NASA's Apollo 17 flight documents, it successfully converted complex, multi-oriented pages into well-structured Markdown. The project is open-source and available on GitHub. Feedback is welcome.
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