Customizing Models
From prompt engineering to fine-tuning to building autonomous agents.
- Step 1
Prompt Engineering Basics
Learn the core techniques for writing effective prompts: system messages, few-shot examples, and structured instructions.
beginner 7 min read - Step 2
Fine-Tuning vs Prompt Engineering
Learn when to shape an LLM with prompts versus when to change its behavior with fine-tuning, and the trade-offs of each.
intermediate 10 min read - Step 3
PEFT (LoRA) and Fine-Tuning Recipes
Learn why LoRA-style parameter-efficient tuning is the default in practice and how to choose robust fine-tuning recipes.
intermediate 11 min read - Step 4
Instruction Tuning, RLHF, and DPO
Trace how base models become assistants through supervised instruction tuning and preference optimization methods like RLHF and DPO.
advanced 12 min read - Step 5
Tool Use / Function Calling
Understand how models call external code safely and reliably using structured outputs, validation, and execution boundaries.
intermediate 10 min read - Step 6
Agents: Planning, Tool Orchestration, and Guardrails
Learn how LLM agents execute multi-step workflows with planning, tool loops, recovery logic, and safety boundaries.
advanced 12 min read