What you will learn

• Explain how next-token prediction, masked-language modeling, and contextual embeddings support LLM classification.

• Translate a marketing research question into a closed label set with definitions and borderline-case rules, and design prompts that return exactly one valid, machine-readable classification label.

• Validate a sample, execute efficient batch inference through an API or vLLM workflow, and select an appropriate fine-tuning workflow when prompt-based classification is insufficient.

• Evaluate prompting and fine-tuning with accuracy, macro F1, class-level errors, and audits of disputed gold labels.