Session 03: Data Preparation for AI

Description

Data Preparation for AI: Engineering Raw Application Data into Clean Model Context High-performing AI models and retrieval systems are only as good as the data pipelines feeding them. If you dump raw, unstructured database logs, messy document dumps, or unparsed JSON directly into an embedding model or LLM context window, your system will suffer from hallucination, high API costs, and context bloat. In this session, we complete Module 1 (Data Infrastructure & Pipelines) by mastering Data Preparation for AI. We transition from streaming raw database events to transforming, chunking, and sanitizing unstructured and semi-structured data into clean, model-ready context representations. What We’ll Cover Deterministic vs. Semantic Chunking: Beyond fixed-character splits—exploring header-aware, markdown-based, and semantic boundary chunking to preserve logical context. Feature & Metadata Extraction: Enriching text chunks with structured metadata (timestamps, tenant IDs, categories, and permissions) to enable high-precision filtering during context retrieval. Zero-Trust PII Redaction & Masking: Implementing automated regex and model-assisted privacy filters to strip sensitive personal data (national IDs, phone numbers, financial credentials) before data reaches vector indices or external LLM APIs. Data Quality & Deduplication: Strategies for detecting near-duplicate content, normalizing text encodings, and handling schema drift in unstructured data feeds. The Hands-On Codelab In this session’s interactive engineering workshop, we will build an automated TypeScript data sanitization and chunking engine. Together, we will: 1. Build a Multi-Strategy Chunking Engine: Implement and compare fixed-length, sliding-window, and semantic header-aware chunking routines. 2. Automate PII Sanitization: Build a zero-trust sanitization pipeline that detects and redacts personal data (PII) before storage. 3. Enrich Chunks with Metadata: Attach typed, versioned metadata envelopes (zod) to every chunk for downstream filtering. 4. Validate Output Readiness: Run quality checks against processed chunks to ensure they meet embedding and retrieval standards. By 4:00 PM, you will have built an automated data preparation pipeline that converts raw application outputs into production-grade, privacy-compliant AI context! Who Should Attend? Software developers, backend engineers, technical architects, and data practitioners who want to build clean, secure, and production-ready data pipelines for intelligent systems.