LLM Fine Tuning Tutorial (Free Labs)
? Fine-Tune LLMs & Build Real AI Agents — https://kode.wiki/4cHnB48
Prompt engineering is fragile. Users can override your system prompt, break character, and inject instructions you never intended. Fine-tuning actually changes the model's weights — embedding behavior directly into how it thinks.
This video walks you through why fine-tuning beats prompt engineering for production AI agents, how LoRA and QLoRA make it feasible on consumer hardware, and how to build a Taco Drive-Through agent that stays on topic and resists jailbreaks — inside a real KodeKloud hands-on lab.
No theory overload. Just structured, practical learning from the problem all the way to alignment testing.
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? WHAT YOU'LL LEARN IN THIS VIDEO
─────────────────────────────────────────
✅ How prompt engineering gets hacked and why fine-tuning is the fix
✅ How RLHF turned GPT-3 into ChatGPT
✅ Real use cases: guaranteed JSON output, brand agents, and game NPCs
✅ How LoRA and QLoRA freeze base parameters and add lightweight adapter layers
✅ All 6 Fine-Tuning steps: prompt problem → data prep → LoRA config → training → evaluation → alignment
? FREE HANDS-ON LAB — https://kode.wiki/4cHnB48
Practice everything in a real sandbox. No local setup, no credit card, no surprises.
GPU environment, dependencies, and all lab tasks are pre-configured and ready to go.
⏱️ TIMESTAMPS
00:00 – Introduction to Fine-Tuning LLMs
00:45 – Prompt Engineering: What It Is and Why It Falls Short
01:40 – Fine-Tuning Explained
02:03 – Real Use Cases
02:45 – LoRA and QLoRA
03:12 – Lab Intro: Taco Drive-Through Agent
04:16 – Lab - Setting up the environment
04:35 – Task 1: The Prompt Engineering Problem
05:40 – Task 2: Preparing Training Data
06:20 – Task 3: Configuring LoRA
07:35 – Task 4: Training with LoRA
08:22 – Task 5: Test Fine-Tuned Agent
09:03 – Task 6: Create DPO Preference Data
10:11 – Key Takeaways
#LLMFineTuning #LoRA #QLoRA #AIAgent #PromptEngineering #RLHF #KodeKloud #MachineLearning #GenerativeAI #DeepLearning #AITutorial #MLOps #OpenAI #FineTuneGPT #AIEngineering #HandsOnLab #LargeLanguageModels #AITraining #LearnAI #DevOpsAI #NLP #LLMTraining #ParameterEfficientFineTuning #CloudAI #AIJailbreak
Prompt engineering is fragile. Users can override your system prompt, break character, and inject instructions you never intended. Fine-tuning actually changes the model's weights — embedding behavior directly into how it thinks.
This video walks you through why fine-tuning beats prompt engineering for production AI agents, how LoRA and QLoRA make it feasible on consumer hardware, and how to build a Taco Drive-Through agent that stays on topic and resists jailbreaks — inside a real KodeKloud hands-on lab.
No theory overload. Just structured, practical learning from the problem all the way to alignment testing.
─────────────────────────────────────────
? WHAT YOU'LL LEARN IN THIS VIDEO
─────────────────────────────────────────
✅ How prompt engineering gets hacked and why fine-tuning is the fix
✅ How RLHF turned GPT-3 into ChatGPT
✅ Real use cases: guaranteed JSON output, brand agents, and game NPCs
✅ How LoRA and QLoRA freeze base parameters and add lightweight adapter layers
✅ All 6 Fine-Tuning steps: prompt problem → data prep → LoRA config → training → evaluation → alignment
? FREE HANDS-ON LAB — https://kode.wiki/4cHnB48
Practice everything in a real sandbox. No local setup, no credit card, no surprises.
GPU environment, dependencies, and all lab tasks are pre-configured and ready to go.
⏱️ TIMESTAMPS
00:00 – Introduction to Fine-Tuning LLMs
00:45 – Prompt Engineering: What It Is and Why It Falls Short
01:40 – Fine-Tuning Explained
02:03 – Real Use Cases
02:45 – LoRA and QLoRA
03:12 – Lab Intro: Taco Drive-Through Agent
04:16 – Lab - Setting up the environment
04:35 – Task 1: The Prompt Engineering Problem
05:40 – Task 2: Preparing Training Data
06:20 – Task 3: Configuring LoRA
07:35 – Task 4: Training with LoRA
08:22 – Task 5: Test Fine-Tuned Agent
09:03 – Task 6: Create DPO Preference Data
10:11 – Key Takeaways
#LLMFineTuning #LoRA #QLoRA #AIAgent #PromptEngineering #RLHF #KodeKloud #MachineLearning #GenerativeAI #DeepLearning #AITutorial #MLOps #OpenAI #FineTuneGPT #AIEngineering #HandsOnLab #LargeLanguageModels #AITraining #LearnAI #DevOpsAI #NLP #LLMTraining #ParameterEfficientFineTuning #CloudAI #AIJailbreak
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