OpenAI has released an official prompt engineering guide to help users get better results from ChatGPT. This comprehensive tutorial simplifies the strategies and tactics outlined in the guide, providing practical prompts and explanations tailored for everyday users, not just developers.
Write Clear Instructions
Clear instructions are the cornerstone of effective communication with AI models like ChatGPT. By providing detailed, unambiguous directives, users can significantly enhance the quality and relevance of the responses they receive.
Include Details in Your Questions
Adding more context and details to your prompts makes it easier for ChatGPT to generate accurate and helpful answers. Instead of vague or overly broad questions, aim for specificity.
- Specify the context
- Add relevant details
- Clarify your objective
Adopt a Persona
Requesting ChatGPT to adopt a specific persona can lead to more engaging and tailored responses. This tactic involves instructing the AI to respond with a certain tone, style, or perspective.
- Business coach
- Friendly scientist
- Investment banker
Use Delimiters for Complex Prompts
For intricate requests that involve multiple parts or steps, using delimiters helps in organising your prompt effectively. This ensures clarity in instructions and improves the AI's response accuracy.
- Triple quotation marks
- HTML/XML-like tags
- Specific formatting requests
Specify the Steps Required
When your task involves a sequence of actions, explicitly outlining each step can guide ChatGPT to produce structured and coherent outputs. This method is particularly useful for procedural or instructional content.
- List each step clearly
- Use sequential numbering
- Provide context for each step
Provide Examples
Offering examples of the desired output can significantly improve the accuracy of ChatGPT's responses. This approach gives the AI a clear model to emulate, enhancing the relevance of its answers.
- Show desired answer format
- Illustrate tone or style
- Clarify complex concepts
Specify Desired Output Length
Defining the length of the desired response can help in obtaining concise and to-the-point answers from ChatGPT. Whether you prefer brevity or detailed explanations, setting expectations upfront can lead to more satisfactory outcomes.
- Two paragraphs
- Three bullet points
- Specific word count
Provide Reference Text
Incorporating reference texts into your prompts can direct ChatGPT to use specific sources or information, making the responses more accurate and relevant to your needs.
Instruct Using Reference Text
When you need ChatGPT to consider particular information or data, providing it as reference text ensures that the responses are grounded in the specified content.
- Include article excerpts
- Specify web pages
- Use direct quotations
Answer with Citations from Reference Text
For academic or research-oriented queries, asking ChatGPT to include citations from the provided reference text can add credibility and traceability to the responses.
- Request citations explicitly
- Provide clear reference material
- Clarify citation format
Split Complex Tasks into Simpler Subtasks
Dividing complex tasks into more manageable subtasks can significantly enhance the effectiveness of your interactions with AI models. This approach not only streamlines the problem-solving process but also ensures that each aspect of the task receives the attention it deserves.
Identify Primary Categories
Beginning with broad classification helps in narrowing down the focus areas. For instance, categorising a query into technical support, billing, or account management directs the subsequent steps more precisely.
Break Down Into Secondary Categories
Once the primary category is identified, further breaking it down into secondary categories like troubleshooting, device compatibility, or software updates can pinpoint the exact nature of the problem. This step-by-step refinement leads to more accurate solutions.
Sequential Prompting
For tasks that are too complex to fit into a single prompt, consider using a sequence of prompts. Start with a broad prompt to categorise the issue, followed by more specific prompts that drill down into the details.
- Classify the issue
- Identify subcategories
- Address each part separately
Summarise and Construct
For tasks requiring a comprehensive understanding of large amounts of text, summarising individual sections before attempting a complete overview can be effective. This piecewise approach accommodates the model's limitations and ensures thorough coverage.
- Summarise individual sections
- Compile summaries for an overall view
Give Models Time to Think
Allowing AI models sufficient time to "think" or process the information can lead to more accurate and thoughtful responses. This involves structuring prompts in a way that encourages the model to consider each step carefully before arriving at a conclusion.
Work Out Solutions Independently
Encouraging the model to first work out its own solution to a problem before comparing it to a provided solution can prevent premature conclusions. This method mirrors the process of careful consideration and analysis.
Use Inner Monologue
Incorporating an inner monologue into the prompt structure can guide the model to reflect on the problem-solving process internally. This technique helps in refining the model's output by ensuring a deeper level of processing.
- Encourage independent problem-solving
- Compare and evaluate solutions
- Reflect internally before responding
Iterative Prompting
Asking the model to review its previous responses and check for possible oversights or errors can enhance the quality of the output. This iterative process mimics the human practice of revising and improving one's work over time.
- Review previous responses
- Identify and correct errors
- Refine the output iteratively
By breaking down complex tasks into simpler subtasks and giving models time to think, users can significantly improve the effectiveness and accuracy of their interactions with AI systems. These strategies foster a more structured and thoughtful approach to prompt engineering, leading to better problem-solving outcomes.
Use External Tools for Better Knowledge Retrieval
Enhancing ChatGPT's knowledge retrieval capabilities can significantly improve the quality of interactions and responses. Leveraging external tools is a powerful strategy to achieve this.
Integrating Knowledge Bases
For developers, incorporating external knowledge bases through API calls can broaden the scope of ChatGPT's responses. This allows access to specialised information beyond ChatGPT's training data.
Building Custom GPTs for Non-Developers
Non-developers can create their own mini GPTs with a specific knowledge base, such as personal documentation or curated content. This customisation leads to more relevant and tailored responses.
Advanced Features: Actions and Functions
Utilising advanced GPT features like actions and functions can further enhance the interaction by enabling dynamic content generation and more precise control over the responses.
Test Changes Systematically
Improving prompt efficiency and effectiveness is an iterative process. Systematic testing of changes to prompts can lead to significantly better outcomes.
Track and Compare Prompt Variations
By keeping track of prompt modifications and comparing the outputs, users can identify which changes yield better responses. This methodical approach ensures continuous improvement.
A/B Testing for Everyday Users
Even without technical expertise, users can apply basic A/B testing principles. Experimenting with different prompt structures and content can reveal what works best for specific tasks.
Creating a Prompt Library
Maintaining a collection of optimised prompts for various applications, such as sales copy or social media posts, saves time and enhances efficiency by reusing proven prompts.
Whether you're a developer looking to integrate complex functionalities or an everyday user seeking to refine your prompts, these strategies offer pathways to more effective and personalised interactions with ChatGPT. By exploring external tools for knowledge enhancement and adopting a systematic approach to testing changes, users can significantly improve the quality of their outputs.
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By: Andrew Seidel