Unstructured Data Management for LLMs

Prepare text, PDFs, and multimedia content for GenAI training and use.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course covers techniques and best practices for managing unstructured data — including text documents, PDFs, images, audio, and video — to optimize its use in training and deploying large language models (LLMs). Participants will learn how to extract, organize, and preprocess diverse data types to enhance the performance and reliability of Generative AI systems.

Learning Objectives

Participants will be able to:

• Identify challenges related to unstructured data in AI projects

• Extract meaningful information from varied content formats

• Clean, normalize, and annotate unstructured data for LLM training

• Use tools and workflows for multimedia data processing

• Ensure data quality and compliance for diverse data sources

Target Audience

• Data engineers and AI practitioners

• Machine learning engineers

• Content managers and data stewards

• AI project managers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Lectures on unstructured data types and challenges

• Hands-on labs with data extraction and preprocessing tools

• Group exercises on data normalization and annotation

• Case studies of real-world unstructured data projects

Materials Provided

• Sample unstructured datasets (text, PDFs, multimedia)

• Tools and scripts for data processing

• Best practice guidelines for data management

Outcomes

• Proficiency in preparing unstructured data for LLMs

• Ability to design workflows for multimodal data integration

• Enhanced data quality leading to better GenAI model outputs

• Awareness of ethical and compliance considerations

Outline / Content

Day 1: Introduction to Unstructured Data

• Types and sources of unstructured data

• Challenges and opportunities in AI training

Day 2: Data Extraction and Preprocessing

• Techniques for extracting text from PDFs and images

• Audio and video preprocessing basics

Day 3: Data Annotation and Normalization

• Annotating unstructured data for training

• Standardizing formats and metadata

Day 4: Integration and Compliance

• Combining multimodal data for LLMs

• Ensuring privacy, ethics, and regulatory compliance