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Fine-tuning LLM: The Art of AI Tuning

Comprehensive course on Fine-tuning Large Language Models — when to fine-tune, data preparation, fine-tuning on Google Gemini/Vertex AI, OpenAI, and open-source (LoRA/QLoRA). Compare Fine-tuning vs RAG, model evaluation methods, and production deployment. Calculate actual costs.

Introducing the Series

Fine-tuning LLM: The Art of AI Tuning is a course that helps you deeply understand and practice fine-tuning — the technique of fine-tuning large language models for your own domain, task, or brand voice.

🎯 Core questions this course answers:

  • When is fine-tuning needed? When to use RAG? When to combine the two?
  • How much does Fine-tune cost? How is the ROI?
  • How to evaluate fine-tuned models scientifically?
  • How to deploy production effectively?

What will you learn?

Part 1: Overview & Strategy

  • Lesson 1: What is Fine-tuning? Decision framework: Prompt Engineering → RAG → Fine-tuning
  • Lesson 2: Fine-tuning vs RAG — the biggest debate, a practical decision checklist
  • Lesson 3: Fine-tuning costs — detailed price list, ROI calculator, budget planning

Part 2: Data Preparation

  • Lesson 4: Collect & design dataset: JSONL format, synthetic data, quality vs quantity
  • Lesson 5: Data cleaning & augmentation: pipeline cleaning, tokenization, split strategies

Part 3: Fine-tuning on Google Gemini / Vertex AI

  • Lesson 6: Vertex AI setup: project, IAM, billing, pricing breakdown
  • Lesson 7: Fine-tune Gemini Flash step-by-step: upload data → train → deploy → test
  • Lesson 8: Advanced: distillation, hyperparameter optimization, multi-task tuning

Part 4: Fine-tuning on OpenAI & Open-source

  • Lesson 9: Fine-tune OpenAI GPT-4o-mini: API workflow, comparison with Gemini
  • Lesson 10: LoRA & QLoRA: fine-tune open-source models on Google Colab for free

Part 5: Model Evaluation — Methods & Metrics

  • Lesson 11: Metrics: Perplexity, BLEU, ROUGE, BERTScore — when to use what
  • Lesson 12: LLM-as-a-Judge & Human Evaluation — multidimensional evaluation
  • Lesson 13: Evaluation Pipeline: golden test sets, CI/CD, A/B testing, red teaming

Part 6: Production & Best Practices

  • Lesson 14: Deployment: Vertex AI endpoints, vLLM, multi-adapter serving
  • Lesson 15: Common pitfalls: catastrophic forgetting, overfitting, troubleshooting
  • Lesson 16: Capstone: fine-tune end-to-end for real use cases

Special features of the course

TopicsContent
🔥 Google Gemini Focus3 separate articles for Vertex AI — the most powerful platform 2025–2026
💰 Actual costsPrice list, ROI calculator, cost optimization — not just theory
📊 Scientific Evaluation3 articles on evaluation — BLEU, ROUGE, LLM-as-Judge, Human Eval
🤔 Fine-tune vs RAG ​​Full decision framework, case studies, hybrid approach
🔧 Multi-platformGoogle Gemini + OpenAI + LoRA/QLoRA open-source
🚀 Production-readyDeployment, monitoring, A/B testing, drift detection

Input required

  • Intermediate Python (async/await, file I/O, JSON handling)
  • Basic understanding of LLM (knows prompt engineering, API calls)
  • Google Cloud account (free trial $300 credit enough for the whole course)
  • OpenAI account (for lesson 9)
  • Google Colab (for lesson 10 — free)

Tools used

Python 3.11+           | Ngôn ngữ chính
Google Cloud / Vertex AI | Fine-tune Gemini models
OpenAI API             | Fine-tune GPT-4o-mini
Hugging Face           | Transformers, PEFT, datasets
Unsloth / Axolotl      | Optimized LoRA training
Weights & Biases       | Experiment tracking
Google Colab           | Free GPU cho hands-on
BERTScore / ROUGE      | Evaluation metrics
LangSmith              | LLM-as-a-Judge pipeline

Compare 3 AI series

AI & LLM SeriesBuild AI AgentsFine-tuning LLM
FocusLLM TheoryBuild AgentRefine Model
ObjectNewbieKnow basic LLMKnow basic LLM
OutputUnderstanding LLMPortfolio AgentsCustom AI Models
TechnologyPyTorch, TransformersLangGraph, CrewAIVertex AI, LoRA
Difficulty level⭐⭐⭐⭐⭐⭐⭐⭐

Part 1: Overview & Strategy — When to Fine-tune?

Lesson 1: What is Fine-tuning? — Landscape & Why you don't need it (yet).90 minLesson 2: Fine-tuning vs RAG — The biggest AI debate of 2025120 minLesson 3: Fine-tuning costs — Calculate ROI before starting90 min

Part 2: Data Preparation — The foundation of all success

Lesson 4: Collect & Design Dataset for Fine-tuning150 minLesson 5: Data Cleaning & Augmentation — From "garbage" to "gold"120 min

Part 3: Fine-tuning on Google Gemini / Vertex AI

Lesson 6: Google Vertex AI Setup — Environment & Pricing120 minLesson 7: Fine-tune Gemini Flash — Supervised Tuning Step-by-step180 minLesson 8: Fine-tune Gemini for Production — Advanced Techniques150 min

Part 4: Fine-tuning on OpenAI & other Platforms

Lesson 9: Fine-tune on OpenAI — GPT-4o-mini & GPT-4o150 minLesson 10: LoRA & QLoRA — Fine-tune Open-source Models210 min

Part 5: Model Evaluation — Methods & Metrics

Lesson 11: LLM Evaluation Metrics — From Perplexity to BERTScore150 minLesson 12: LLM-as-a-Judge & Human Evaluation150 minLesson 13: Evaluation Pipeline — Test Fine-tuned Model like "Pro"180 min

Part 6: Production & Best Practices

Lesson 14: Deployment — Serve Fine-tuned Model effectively150 minLesson 15: Common Pitfalls & Troubleshooting120 minLesson 16: Capstone — Fine-tune Model for actual Use Cases240 min