In the rapidly evolving world of artificial intelligence, creating effective custom GPTs, agents, and chatbots has become an essential skill. The key to their success lies in the art of prompt engineering – crafting instructions that ensure reliable and accurate performance. This article will guide you through the process of writing clear, effective instructions for your AI agemts, from simple chatbots to complex custom GPTs.
Basic Structure for Simple Agents
For straightforward agents, a general structure can be employed that covers most use cases. This structure consists of five key components:
Goal: Define the overarching aim of the agent. What is its primary purpose?
Persona: Outline any specific characteristics, including the chatbot's name, tone, and style of communication.
Directions: Provide step-by-step instructions for the agent to follow. For example, "If the user says X, always respond with Y."
Definitions: Clarify any terms or concepts that the agent needs to understand.
Notes: Include any additional information that doesn't fit into the above categories, such as output format requirements (e.g., "Always output in JSON").
This basic structure will suffice for about 90% of simple agent use cases. However, as we move into more complex territory, additional considerations come into play.
Enhancing Instructions for Complex Agents
When dealing with more sophisticated custom GPTs or agents, implementing effective prompt engineering practices becomes crucial. Here are key guidelines to enhance your instructions:
Simplifying Complex Instructions
Breaking down multi-step instructions:
Complex tasks should be divided into simpler, more manageable steps. This ensures that the model can follow instructions accurately without getting overwhelmed.
Using trigger/instruction pairs:
Implement "trigger/instruction pairs" separated by delimiters to improve reliability in following steps without merging or skipping them. For example:
Trigger: User submits information
Instruction: Analyze information for themes
Trigger: Themes analyzed
Instruction: Leverage themes analyzed to provide a summary in bullet point form of the recommendations you'd give
Structuring for Clarity
Second-level instructions:
Break down secondary instructions into separate steps for better execution. This helps the model process information in a more organized manner.
Using delimiters:
Employ delimiters between instruction sets and for call-outs of few-shot examples. This enhances clarity and helps the model distinguish between different parts of the instructions.
Promoting Attention to Detail
Encouraging thoroughness:
Incorporate phrases like "take your time," "take a deep breath," and "check your work" to encourage the model to be thorough in its responses.
Using strengthening language:
Employ "strengthening language" to highlight critical parts of the instructions, ensuring they are not overlooked. For example, "It is crucial that you..."
Avoiding Negative Instructions
Frame instructions positively to improve adherence and avoid confusion. Instead of saying "Don't do X," say "Always do Y."
Implementing Granular Steps
Break down steps as granularly as possible, especially when multiple actions are required within a single step. This helps ensure that no part of the process is overlooked.
Ensuring Consistency and Clarity
Defining terms and expectations:
Explicitly define terms and definitions you are expecting using few-shot prompting. For example, clarify what constitutes acceptable vs. unacceptable changes to improve consistency in evaluations.
Clarifying classifications:
Provide few-shot examples to clarify any relevant classifications. This helps reduce variability in output and ensures that the model understands the nuances of different categories.