Fine-Tuning LLMs Explained: Prompting vs RAG vs Fine Tuning | Cost, PEFT, RLHF

Description

Fine-tuning LLMs explained from first principles to production. In this video, we walk through how large language models evolve from generalist to domain expert, when you should fine-tune vs use prompting or RAG, and the real-world cost, quality, and safety trade-offs involved. This tutorial covers the complete LLM fine-tuning lifecycle used in real production systems. 🔹 What You’ll Learn * What fine-tuning LLMs really means (generalist → specialist) * Prompting vs RAG vs Fine-Tuning — when to use each * Why and when fine-tuning makes sense (and when it doesn’t) * Fine-tuning costs and efficiency trade-offs * Getting data right: real vs synthetic data, cleaning, preparation * Full model fine-tuning vs PEFT (Parameter Efficient Fine-Tuning) * Catastrophic forgetting in full fine-tuning * PEFT methods: LoRA, Adapters, Prompt Tuning * Quality & Safety in fine-tuned models * Alignment and RLHF: reward models, optimization loops * Risks in alignment: reward hacking, annotator bias * Comprehensive evaluation: offline metrics, human eval, red-teaming * LLMs in production: monitoring, learning, and continuous improvement * Drift in production systems: * Data drift * Model drift * Cost & latency drift * Building a continuous observability → fine-tuning loop This video is ideal for: * Software engineers learning AI * ML engineers & data scientists * QA & testing professionals working with AI systems * Anyone deploying LLMs in production If you found this useful, like, share, and subscribe for more software, testing, and AI tutorials. 💬 Comment below: Do you currently use prompting, RAG, or fine-tuning in your projects — and why? Keywords covered * fine tuning LLM * LLM fine tuning explained * fine tuning large language models * LLM fine tuning tutorial * fine tuning vs RAG * prompt engineering vs fine tuning * LLM fine tuning cost * when to fine tune LLM * LLM fine tuning workflow * LLM production monitoring * PEFT fine tuning explained * LoRA fine tuning tutorial * adapters vs lora * catastrophic forgetting LLM * RLHF explained * reward model training LLM * alignment in LLMs * LLM evaluation metrics * model drift in LLMs * monitoring LLM in production AI / Machine Learning Tutorials https://www.youtube.com/playlist?list=PLc3SzDYhhiGWsHYGhnGwGSX3C-EXUmotP Artificial intelligence for Software Testing and Test Automation https://www.youtube.com/playlist?list=PLc3SzDYhhiGXcuIxOewDNJGZSbqXd7fkW) ***** Join this channel to get access to perks like exclusive content: https://youtube.com/c/SoftwareandTestingTraining/join ***** Software Testing Tutorials #shorts (Mini Software Testing course): https://www.youtube.com/playlist?list=PLc3SzDYhhiGUPN9xL4JnWKikcR0JYe0eh Software Testing Tutorials (complete set): https://www.youtube.com/playlist?list=PLc3SzDYhhiGWuMK03uYO-UENAx0s2woWM Selenium Tutorials (Selenium Java): https://www.youtube.com/playlist?list=PLc3SzDYhhiGXpUQyWlYK2JynPYWKkNUE_ Selenium Videos (Selenium Python Tutorials complete set) : https://www.youtube.com/playlist?list=PLc3SzDYhhiGUPPWt_rIVszepL1nMTbDaW Test Automation: https://www.youtube.com/playlist?list=PLc3SzDYhhiGXVcy8EcrTSfwsC-v8EUZvg Subscribe to Software and Testing Training channel: https://youtube.com/c/SoftwareandTestingTraining?sub_confirmation=1 Software and Testing Training: Online training in AI, Gen AI, ML, DL, Data Science, Python programming, VB scripting, Perl scripting, Big Data, SQL, HTML, XML, Selenium Python, Selenium with Java, SoapUI, LoadRunner and JMeter automated software testing tools, software testing training, Database testing, QA, domain knowledge and others #softwareandtestingtraining #fourthindustrialrevolution #inderpsingh Website (blog): https://fourth-industrial-revolution.blogspot.com/ LinkedIn: https://www.linkedin.com/in/inderpsingh/