157 minutesFine Tuning LLM Models – Generative AI Course
You will be able to run quantisation, LoRA and QLoRA fine-tunes end to end on hardware most developers already have.
IBM Technology. A clear whiteboard pass through all three techniques with their respective costs — retrieval latency, training compute and catastrophic forgetting, the limits of prompt-only solutions — and the combinations that actually make sense in production. The closing example of a legal AI system using all three is almost exactly the article's "when to combine" argument.
This is a stable decision framework. Use it before buying infrastructure or starting a fine-tune project; most wrong choices come from misdiagnosing freshness, attribution, style or behavior problems.
Choose between prompt engineering, RAG, fine-tuning or a combination based on the actual failure mode.
Know what each technique means at a high level.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
157 minutesYou will be able to run quantisation, LoRA and QLoRA fine-tunes end to end on hardware most developers already have.
15 minutesDesign simpler agent loops with clear stopping rules, task boundaries and human control points.
190 minutesYou can build a LangGraph agent with typed state, conditional routing, checkpoints and tool use, and feel where the explicitness pays off.
Hand-picked external courses that go deeper on this topic.
Emory University Goizueta Business School faculty
The deeper, university-level counterpart to our beginner HubSpot marketing pick — Emory's business-school treatment goes past 'how to prompt' into training generative models for brand-specific output, the purchase-funnel economics of AI-generated content, and a full module of genuine skepticism about when generative AI is and isn't worth using in marketing.
IBM AI Academy
The genAI-era answer to the executive-strategy question. Three short courses aimed squarely at business leaders — no technical background required — on where generative AI creates value, how to govern it responsibly, and how to turn a vague "we should use AI" into a concrete, defensible use case. Rated 4.6 across ~700 reviews. Best taken before your next AI budget decision.