Prompt

Few-Shot Prompting

• Few-shot prompting involves providing a few labeled examples in the prompt. This differs from traditional few-shot learning, which entails fine-tuning the LLM with a few samples for a novel problem. This approach lessens the reliance on large labeled datasets by allowing models to swiftly adapt and produce precise predictions for new classes with a small number of labeled samples.

The movie was fantastic! - Positive
I didn't enjoy the food at all. - Negative
Amazing vacation, I had a great time! - Positive
She looks upset and angry. - Negative
The book was hard to put down. - {sentiment_label}

Chain-of-Thought

• The process involves crafting prompts that guide the AI to break down a problem into smaller, manageable parts.

• The prompt instruct the model that how to think and proceed for the next step

• The model sequentially addresses each part by building upon the previous steps until it concludes.

“List three reasons why people might prefer working from home.”
“For each reason you mentioned, explain the benefits and positive effects on the individual and/or the organization.”
“Now, consider the challenges of working from home. Identify three potential disadvantages or negative effects.”
“For each of these challenges, discuss how individuals and organizations can mitigate or address them.”