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Fine-Tuning Large Language Models

Learn when fine tuning beats prompting or retrieval, how to prepare training data, and how to run and evaluate a fine tune without wasting budget.

Material Overview
Fine-Tuning Large Language Models

Fine tuning is powerful and frequently misused. You will learn to judge whether prompting, retrieval, or fine tuning fits a problem, then prepare and clean a training dataset, choose between full fine tuning and parameter efficient methods such as LoRA, and run training with sensible hyperparameters. Evaluation, overfitting checks, versioning, and serving costs are covered so your results still hold up once real users arrive.

Curriculum

Learning Path

Each level is enrolled separately. Work through them in order, or start at the level that matches your experience.

  1. Level 01

    Beginner

    Purpose

    Learn what fine tuning actually changes in a model and when it is the wrong answer to a problem.

    Outcome

    You can decide whether a problem needs fine tuning before spending anything on it.

    What You'll Learn
    • Compare prompting, retrieval, and fine tuning against the same problem
    • Understand what a fine tune can teach a model and what it cannot add
    • Estimate the data, time, and budget a small fine tune requires

    189 CREDS

  2. Level 02

    Intermediate

    Purpose

    Build the training dataset, which decides the outcome far more than any hyperparameter setting.

    Outcome

    You can produce a training dataset that is clean, balanced, and honestly split.

    What You'll Learn
    • Collect, clean, and format examples in the structure the training job expects
    • Balance the dataset and remove duplicates, errors, and leaked answers
    • Hold back a validation split that reflects real usage

    498 CREDS

  3. Level 03

    Advanced

    Purpose

    Run the training itself and read the results without fooling yourself about what improved.

    Outcome

    You can run a fine tuning job and explain, with evidence, what it improved and what it broke.

    What You'll Learn
    • Choose between full fine tuning and parameter efficient methods such as LoRA
    • Set learning rate, epochs, and batch size and read the loss curves
    • Detect overfitting and catastrophic forgetting before deployment

    669 CREDS

  4. Level 04

    Expert

    Purpose

    Take a fine tuned model into production with evaluation, versioning, and a realistic cost model.

    Outcome

    You can own a fine tuned model in production and retire or retrain it on evidence.

    What You'll Learn
    • Evaluate against held out tasks and compare with the base model honestly
    • Version models and datasets so any result can be reproduced
    • Plan serving cost, latency, and a rollback path for when quality drops

    1,054 CREDS