Prompting Engineering Techniques
Prompting Engineering Techniques
In this section, we will outline different prompting techniques. Prompting engineering techniques are strategies used to guide the behavior of AI models like LLMs in generating desired responses.
Prompting Techniques
Some prompting techniques are as follows:
- Zero-shot Prompting
- Few-shot Prompting
- Chain-of-Thought(CoT)
- Role-based Prompting
- Self-consistency
- General knowledge Prompting
- Tree of Thoughts
Zero-shot Prompting: Providing the AI model with a task without any previous examples.
Few-shot Prompting: Give the AI model a few examples to illustrate the task before asking it to perform.
Chain-of-Thought (CoT) Prompting: Encouraging the model to “think aloud” or show its reasoning process step by step.
Role-based prompting: involves tailoring prompts or instructions according to individuals’ roles or responsibilities within an organization.
Self-Consistency: Asking the model the same question multiple times to ensure consistent answers.
Generated Knowledge Prompting: Using the model’s ability to generate new content based on the learned information.
Tree of Thoughts: In this approach, the model self-evaluates the progress of intermediate steps through a tree of intermediate thoughts to solve the reasoning task. This method combines tree search algorithms and tree process
algorithms like backtracking.
