? Customize LLMs & Agents for FREE — https://kode.wiki/3QcX45W
Most teams rely on prompt engineering. The ones building reliable production AI agents are fine-tuning their models.
This video walks you through the complete data preparation pipeline for fine-tuning LLMs using LoRA and QLoRA, inside a real hands-on KodeKloud lab with a live Secure Ops scenario.
No fluff. No theory overload. Just structured, hands-on learning starting from why your training data format matters, all the way to testing your dataset against a live LLM for alignment scoring.
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? WHAT YOU'LL LEARN IN THIS VIDEO
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✅ Why fine-tuning beats prompt engineering for enterprise AI agents
✅ How LoRA and QLoRA work and why they make fine-tuning viable on consumer GPUs
✅ Memory math breakdown: 1B, 7B, and 70B parameter models with QLoRA
✅ How to transform raw security logs into JSONL training data
? FREE HANDS-ON LAB INCLUDED — https://kode.wiki/3QcX45W
Practice everything in a real sandbox environment with no local setup, no credit card, no surprises.
GPU environment, dependencies, and all lab tasks are already configured and ready to go.
⏱️ TIMESTAMPS
00:00 – Introduction: Why Fine-Tuning Beats Prompt Engineering
00:38 – Hardware Requirements:
01:04 – LoRA and QLoRA Explained
02:10 – Training Data Requirements
03:31 – Lab Intro - Customize LLMs & Agents
04:54 – Task 0: Environment Setup
05:18 – Task 1: Why Data Format Matters
06:14 – Task 2: Log Transformation
07:38 – Task 3: Agent Persona Training Data
08:50 – Task 4: Classification Dataset
09:41 – Task 5: Data Quality Validation
10:33 – Task 6: Verify with LLM Inference
11:38 – Key Takeaways
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