Why LLM Training Is Hard and How to Organize the Team to Hit Time and Cost Targets

You are the product manager on a project to train a large language model. Answer in two parts: first explain what makes training LLMs difficult, then describe how you would organize the team so that, given those difficulties, the training objectives are met on schedule and within budget. ### Clarifying Questions - Does the project pretrain a model from scratch, or adapt an existing base model through fine-tuning and alignment? - Are the training objectives already defined (target capabilities and evaluation thresholds), or is setting them part of my job? - Are the deadline and budget fixed, and does the budget cover only compute or also people, data licensing and annotation? - Is the model meant to be a general assistant, a specialist for a domain such as code, or a feature inside one product? ### Part 1 — What Makes LLM Training Hard List the main challenges across the training lifecycle and explain how each one puts quality, schedule or cost at risk. ```hint Walk the lifecycle Go stage by stage: assembling the data, sizing the model and the run, running the job on a large cluster, post-training, and evaluation. Each stage fails in its own way and at its own price. ``` #### What This Part Should Cover - Breadth across data, compute and infrastructure, training stability, post-training and evaluation rather than a single area - For each challenge, its concrete consequence for quality, time or money - Sound technical grounding, such as why a problem discovered late in a long run is so expensive ### Part 2 — Organizing the Team for Schedule and Budget Design the team structure, ownership and working process you would use so that the challenges from Part 1 are managed and the objectives are delivered on time and on budget. ```hint Give every risk an owner and an early warning For each major challenge from Part 1, name who owns it and the earliest, cheapest point at which the team would find out it is going wrong. ``` #### What This Part Should Cover - Workstreams and owners mapped to the Part 1 challenges, with clear decision rights while the run is in progress - A phased plan with gates where cheap experiments reduce the risk of the expensive run - How compute and other costs are estimated, allocated, tracked and protected with a reserve - Levers agreed in advance for when schedule or budget slips ### What a Strong Answer Covers - Starting from a defined objective: which capabilities, measured by which evaluations, for which product use - A clear split between the product manager's role and the research, engineering and program management roles - Build-versus-adapt reasoning about whether pretraining from scratch is needed - Explicit trade-offs among model quality, time and cost - Safety, legal and privacy work built into the plan from the start ### Follow-up Questions - Midway through pretraining, the loss spikes and checkpoint evaluations stall. What happens next, and who makes the call? - Leadership cuts the compute budget mid-project. Which levers do you pull first, and what quality do you give up? - Public benchmark scores look strong but your product evaluations lag. Is the model ready to ship?