Course Description:

This course is designed to teach participants how to extract and standardize content from a broad array of document types including PDFs, PowerPoints, Word documents, and HTML files. It also covers the addition of metadata to enrich content, thereby supporting improved search capabilities and augmented generation results. Further, the course delves into document image analysis techniques like layout detection and vision and table transformers, aiming to equip learners with the skills necessary to preprocess various formats for better integration into large language model (LLM) Retrieval Augmented Generation (RAG) systems.

What Students Will Learn:

  • Methods to preprocess diverse unstructured data for LLM application development.
  • Skills to extract and normalize documents into a common JSON format and enrich this data with metadata.
  • Techniques in document image analysis to effectively understand and handle PDFs, images, and tables.
  • Building a functional RAG bot capable of processing multiple document types.
  • Implementing enhanced LLM RAG pipelines to incorporate various file formats like Excel, Word, PowerPoint, PDF, and EPUB.

Prerequisites:

Participants should have a basic understanding of data processing, familiarity with JSON format, and some experience with programming concepts. Knowledge of document management and previous experience in handling different data types are advantageous but not strictly required.

Course Coverage:

  • Data preprocessing techniques for varied document types.
  • JSON formatting and metadata enrichment.
  • Document image analysis including layout detection and vision transformers.
  • Practical implementation of a RAG bot for document ingestion

Who This Course Is For:

This course is ideal for individuals interested in enhancing their understanding and skills in processing diverse unstructured data types for the development of high-performance LLM RAG systems. It is particularly beneficial for data scientists, AI developers, and those in roles involving extensive document handling and manipulation.

Application of Learned Skills:

Skills acquired from this course can be applied in various real-world scenarios like building more robust data retrieval systems, enhancing document management efficiency in corporations, and improving the functionality and reach of AI-driven applications across industries.