Generative AI & LLMs

Fine-Tuning & LLMOps

9 practice questions. Free questions open a full answer guide; the rest unlock with Pro.

  • You've fine-tuned per-tenant variants of an open-weight LLM and now have to serve dozens of them in production cost-effectively, and re-evaluate each variant whenever the base model is upgraded. How would you design the serving and the lifecycle around these fine-tuned models? Senior level
  • You've fine-tuned a model that beats the base model on your eval. How do you roll it out to production and monitor it so a regression — including the fine-tune quietly losing general capabilities — doesn't reach all your users?Go Pro Mid level
  • When would you choose to fine-tune an LLM instead of relying on prompt engineering or retrieval-augmented generation?Go Pro Junior level
  • A team has decided to fine-tune an open-weight LLM for a domain task and is debating full fine-tuning versus a parameter-efficient method, and whether they even need preference tuning. How do you reason through the method choice, and how do you keep the model from losing its general abilities?Go Pro Senior level
  • A stakeholder wants you to fine-tune an LLM for a new feature. How do you decide whether fine-tuning is the right move, and if it is, how do you choose between full fine-tuning and a parameter-efficient method like LoRA?Go Pro Mid level
  • Leadership wants to fine-tune an open-weight model and is convinced more training examples will fix the quality problem. As the senior engineer, how do you approach the dataset and evaluation for a fine-tune so you can actually tell whether it worked and didn't regress the model's general ability?Go Pro Senior level
  • If you fine-tune an LLM on a small dataset and it gets worse at general tasks it used to handle, what's happening and how would you address it?Go Pro Junior level
  • What is LoRA (parameter-efficient fine-tuning), and why is it usually preferred over full fine-tuning of an LLM?Go Pro Junior level
  • You've been asked to fine-tune an LLM so it both follows your output format and matches your team's preferred tone. Would you do that in one step, and how would you sequence the training and curate the data?Go Pro Mid level
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