Prompt Engineering

Clarity and specificity techniques help guide the AI to produce more concise and accurate outputs by focusing on the essential details and avoiding redundant information.

Context setting methods help guide the AI into the context of a specific task or scenario, allowing it to generate relevant and contextually appropriate outputs.

Role assignment strategies help assign specific roles or identities to the AI, such as experts, personas, or fictional characters, to provide context and clarity.

Task framing approaches help structure prompts to clearly define the desired task and expected output format.

Iterative refinement is the process of gradually improving your prompts through repeated testing and adjustment. This approach helps to fine-tune the AI's responses and achieve more accurate and desired outputs.

Constraint definition techniques help guide AI models to produce more focused and accurate outputs by setting clear boundaries and limitations.

Example-based prompting involves providing specific instances or scenarios to guide the AI's response. This technique helps in setting clear expectations and can significantly improve the quality and relevance of the output.

Chain-of-Thought (CoT) prompting is an advanced technique that encourages the model to break down complex problems into step-by-step reasoning processes, leading to more accurate and explainable outputs.

Question refactoring involves rephrasing a question to gain new insights and improve the model's ability to understand and respond to the original question.

Chain multiple prompts to break complex tasks into manageable steps, guide the AI through a series of questions, or create a conversation flow.