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Fine-Tuning LLMs: LoRA, RLHF & Deployment Deep Dive
New
100 students

Fine-Tuning LLMs: LoRA, RLHF & Deployment Deep Dive

Master LoRA, QLoRA, RLHF, DPO & full fine-tuning — dataset prep to production deployment
New
Created byCrack The Interview Co.
Last updated 8/2026
English

What you'll learn

  • Understand when to fine-tune vs. use RAG or prompting, and how to prepare a high-quality fine-tuning dataset
  • Master parameter-efficient techniques like LoRA and QLoRA to fine-tune LLMs efficiently on limited hardware
  • Learn full fine-tuning, RLHF, and DPO to align model behavior with human preferences and safety goals
  • Deploy, evaluate, and maintain a fine-tuned model in production, including monitoring and rollback planning

Included in This Course

600 questions

  • Fine-Tuning Fundamentals & Core Concepts100 questions
  • Data Preparation & Dataset Engineering100 questions
  • Parameter-Efficient Fine-Tuning Techniques100 questions
  • Full Fine-Tuning, RLHF & Alignment Techniques100 questions
  • Fine-Tuning Infrastructure & Training Operations100 questions
  • Evaluation, Deployment & Production Best Practices100 questions

Description

Fine-tuning a large language model is one of the most valuable skills in AI engineering today — but doing it well takes more than following a tutorial. It requires knowing when to fine-tune at all, how to prepare a genuinely good dataset, which technique actually fits your hardware and goal, and how to evaluate and deploy the result responsibly. This course is built as a rigorous, comprehensive practice-test series designed to test and reinforce your knowledge across the entire fine-tuning lifecycle, from first decision to production deployment.

You'll work through six full practice tests, each covering a distinct area:

  1. Fine-Tuning Fundamentals & Core Concepts — when to fine-tune vs. RAG or prompting, transfer learning, domain adaptation, and base model selection

  2. Data Preparation & Dataset Engineering — dataset formats, sourcing strategies, cleaning, PII removal, and validation

  3. Parameter-Efficient Fine-Tuning Techniques — LoRA, QLoRA, prefix tuning, adapter layers, and hyperparameter tuning

  4. Full Fine-Tuning, RLHF & Alignment Techniques — reward models, preference data, DPO, and constitutional AI

  5. Fine-Tuning Infrastructure & Training Operations — hardware planning, DeepSpeed, monitoring, and experiment tracking

  6. Evaluation, Deployment & Production Best Practices — evaluation methodology, A/B testing, canary rollouts, and ongoing monitoring

Each question includes a detailed explanation connecting the concept to related ideas covered elsewhere in the course, so you're not just memorizing facts — you're building a genuinely connected mental model of how fine-tuning actually works end to end.

Who this course is for:

  • This course is for software engineers, ML/AI engineers, and technical practitioners who want a strong, practical understanding of fine-tuning large language models — whether you're deciding between fine-tuning and RAG for a real project, applying LoRA or QLoRA on limited hardware, aligning a model's behavior with RLHF or DPO, or deploying and maintaining a fine-tuned model in production. It's also a good fit for anyone preparing for a technical interview touching on LLM customization, or anyone who wants to validate and reinforce their understanding through rigorous practice questions rather than passive video watching.

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