LLM Training: How to Build, Govern, and Optimize Large Language Models

When you hear LLM training, the process of teaching large language models to understand and generate human-like text using massive datasets and computational power. Also known as pretraining, it's the foundation of every chatbot, summarizer, and code assistant you use today. But most people think it’s just about feeding more data into a model. It’s not. It’s about choosing the right data, locking down who can touch it, and knowing when to stop before you burn through your budget.

Pretraining corpus, the curated collection of text used to train a language model before fine-tuning makes or breaks your results. A sloppy mix of Reddit threads, outdated Wikipedia pages, and scraped blogs? You’ll get a model that hallucinates facts and repeats biases. A tight, domain-specific corpus—say, medical journals for a healthcare bot or legal briefs for a contract analyzer? That’s how you get accuracy without retraining. And it’s not just about volume. It’s about balance: how much code, how much dialogue, how much technical documentation. The posts below show exactly how teams are building these datasets without wasting months.

Then there’s enterprise data governance, the policies, tools, and audits that ensure LLM training data is legal, ethical, and secure. If your model was trained on customer emails, employee chats, or private documents, you’re not just building AI—you’re risking lawsuits. Microsoft Purview, Databricks, and custom metadata trackers aren’t optional anymore. They’re the difference between a pilot project and a production system that survives compliance audits.

And don’t forget model optimization, the practice of reducing size, cost, and latency without losing performance. Compressing a 70-billion-parameter model into a 7-billion one isn’t magic—it’s math. Quantization, pruning, distillation. The right move saves you tens of thousands in cloud bills every month. But switching models too early? You lose accuracy. The posts here break down exactly when to compress, when to swap, and when to just accept that bigger is still better.

LLM training today isn’t a lab experiment. It’s a production discipline. You need to track your data lineage, control who accesses your weights, monitor how your model behaves under real usage, and keep your costs from spiraling. That’s what this collection is for. You’ll find real-world guides on building training data that actually works, setting up governance that passes legal review, and cutting cloud costs without sacrificing quality. No fluff. No theory. Just what works when the clock is ticking and the budget is tight.