Unlocking LLM: Fundamental of Prompt Engineering with LLaMa-2

In this article, we’ll explore the nuances of prompt engineering, particularly focusing on its application with the LLaMa-2 model. We’ll navigate through the key model parameters, the foundational aspects of crafting prompts, and various prompting techniques like zero-shot, few-shot, and the ‘Chain of Thought (CoT)’ method. Recommended approach is to begin with zero-shot prompting. If unsatisfied, transition to few-shot prompting. If neither meets your needs, consider fine-tuning the model to your specific data which is beyond the scope of this discussion.

Key Prompting Techniques Explained!

• Zero-shot Prompting: Zero-shot prompting refers to a method where a language model is given a task or question it hasn’t explicitly been trained on and is expected to provide a relevant response based solely on its pre-existing knowledge.
• Few-shot Prompting: Few-shot prompting involves providing a language model with a small number of examples (or “shots”) of a particular task before presenting it with a new instance of that task. The goal is to guide the model by showing it how similar tasks were performed in the given examples. This helps the model understand and perform the new task more accurately. Essentially, it’s like giving a machine a few examples to help it get the hang…