This workflow demonstrates how a few-shot prompt can be used to perform aspect-based sentiment analysis an how to validate the model's response. It showcases response control techniques by guiding the model with labelled examples and checking whether the predicted output follows the intended format (string or JSON).
The workflow processes product review data for laptops, combining review content with few-shot examples to generate aspect-specific prompts. It then sends these prompts to an LLM, which is expected to identify both the aspect and the corresponding sentiment. After the response is received, validation steps compare the output against the original prompt, checking for consistency.