Building Code Generation LLMs: Data Strategy and Training

Building artificial intelligence models that can write reliable code requires more than just raw data. It demands a rigorous data strategy, specialized training objectives, and strict adherence to privacy and licensing standards. This text-based course guides you through the foundational concepts of curating, filtering, and preparing code-specific datasets to train highly capable and compliant Large Language Models (LLMs). You will understand how to transform raw source code into high-quality training tokens while safeguarding proprietary and personal information. What you'll learn: Understand the core architecture and dataset requirements unique to code generation models; Apply data cleaning, deduplication, and syntax-validation techniques to raw source code; Learn modern training objectives like Fill-in-the-Middle (FIM) to enable inline code completion; Evaluate model performance using standardized benchmarks like HumanEval and pass@k metrics; Address privacy, security, and open-source licensing compliance within your training pipelines; Explore synthetic data generation patterns to augment specialized programming datasets. We start by establishing foundational definitions around code tokenization and dataset curation. From there, you will read through structured explanations of modern pre-training techniques, alignment strategies, and security protocols used to build production-ready code assistants. This course is designed for software engineers, data scientists, and technical product managers new to LLM data pipelines. No prior experience in training foundation models is required. Start reading today to master the data strategies powering the next generation of AI coding tools.