When an LLM’s answer changes depending on training data or knowledge cutoffs, “better prompts” aren’t the only option. This video breaks down three practical ways to improve chatbot output—what each one adds, and what it costs.

• Clarify the differences between RAG, fine-tuning, and prompt engineering as distinct methods for getting better model responses.
• Understand RAG step-by-step (retrieval → augmentation → generation), including how embeddings enable semantic matching across internal documents.
• See what fine-tuning actually changes (model weights via supervised input–output pairs), and why it can be faster at inference time but harder to maintain.
• Learn where prompt engineering helps (format, context, examples) and where it can’t (teaching truly new information).