Data Pipelines for LLMOps: Document Ingestion and Chunking

Building high-performing Language Model applications requires more than just calling an API; it demands high-quality, well-structured data. This text-only course guides you through the foundational data engineering steps of LLMOps, teaching you how to transform unstructured documents into clean, searchable chunks optimized for vector databases and retrieval-augmented generation (RAG). What you'll learn: - Understand the fundamental architecture of LLM data pipelines and the role of LLMOps. - Clean and preprocess raw text from diverse formats like PDFs, HTML, and Markdown. - Implement chunking strategies, including character-based, recursive, and semantic splitting. - Enrich chunks with metadata to improve downstream retrieval relevance and filtering. - Design robust data ingestion pipelines that prepare text for embedding generation. You will start with core concepts of text representation and LLMOps data flows before moving on to practical, text-based code examples that demonstrate parsing, splitting, and structuring documents. This course is ideal for software developers, data enthusiasts, and aspiring AI engineers who want to master the data preparation stage of LLM applications without any prior LLMOps experience. Start reading today to master the critical data foundations that power modern AI systems.