New course with Unstructured: Preprocessing Unstructured Data for LLM Applications

Key Takeaways

I'm delighted to introduce pre-processing unstructured data for LM applications built in partnership with unstructured and taught by Matt Robinson retrieval augmented generation or rag is increasingly adopted by Enterprises in this course you learn techniques for getting many types of unstructured data like text images tables from many different data sources like PDF PowerPoint word and so on in a way that lets your LM rag system access all of this information thus enabling to generate much better answers when building an LM rag system one component that has a huge impact on the result quality but is often overlooked is the work needed to get that data to the LM s company has a lot of different file types and data formats like numeric data and Excel spreadsheets reports in word or PDF presentations in PowerPoint HTML Pages how do you get Ana system to use all of these different types of file formats further a PDF might itself have tables and images inside that PDF and a PowerPoint might also contain tables and images pausing these different formats and normalizing the data for example making sure an image whether from a PDF or a PowerPoint is represented similarly will help your LM better retrieve and use all this information in addition to normalization it also helps to keep the high level structure of data from the original document by Pres observing metadata that tells you for example where a certain table or image had come from in that larger document and also keep track of things like what was the title subheading and so on within that larger document this type of metadata helps your LM rag system do better retrieving as well as reasoning over that data so as you can see there are a lot of important practical details for creating a rack system from different data sources I'm delighted to introduce the instructor Matt Robinson who is head of product at unstructured which provides a rich set of tools for LM data pre-processing Matt has helped many developers build LM applications that use and combine data from diverse sources thanks Andrew I'm really excited to be teaching this course in this course you learn about data pre-processing for llm application development focusing on how to work with different document types like PDFs PowerPoints word docs and HTML you'll learn to extract and normalize content and enrich it with metadata to improve search results this course covers techniques for document image analysis including document layout detection and vision Transformers where you'll learn how to extract and understand tables after learning these Concepts you'll put them all together to build a rag bot out of a corpus that includes PDF PowerPoint and markdown documents this course teaches important techniques to have a huge practical impact on how well your rag system will perform please enjoy the course was