ZappyChat AI

Prompt Library

Role - Goal - Rules - Conversation Flow - Context

Prompt Engineering

Core Elements of a Prompt                                                                       

Designing effective prompts for your chatbot involves a few core components:

Define the Role of the Chatbot: Here, you describe the behavior or role of the AI model. For instance, the chatbot could be a technical support specialist, a customer service representative, a doctor's assistant, etc.

State the Goal: Here, you describe what the AI is trying to accomplish in the conversation. It could be to provide support, help users make purchases, provide accurate health information, and so on.

Provide Context: Here, you provide additional instructions that describe more details of the behavior or rules that the AI should follow. For instance, the bot should always be polite, it should not provide any form of legal advice, etc.

Provide Examples: Examples are useful for informing the AI about the type of conversation it's expected to have. You might provide a few sample inputs and desired outputs.

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Example:

You are an experienced Twitter user, Known for posting highly engaging tweets that lead users to like or retweet the posted tweets.

Write about hiking.

The tweet should use no more than 2 emojis and target nature enthusiasts. It should highlight two advantages of going on a hike regularly

Result:

🌲Nature’s calling! 🥾 Hiking not only strengthens our bond with Mother Earth, but it also boosts our mental & physical health. Let’s embrace the trail and breathe in the magic of the great outdoors! #HikingAdventures #NatureLovers #GetOutside

✅ It’s all about providing (helpful) Context

Adding Meaningful Context

🎯 Prefer short, focused sentences

📌 Add important keywords & avoid unnecessary information

👥 Define the target audience

🗣️ Control tone, Style & length of the output

Zero, One & Few-Shot Prompting

0️⃣ Zero Shot

1️⃣ One Shot

🔢 Few Shot

➡️ Provides no Example

➡️ Provides one Example

➡️ Provides Multiple Examples

✍🏻 “Write a tweet that explains the core idea behind ChatGPT”

✍🏻“Write a tweet that explains the core idea behind ChatGPT. Use a similar tone & structure as I do in my regular tweets. But don’t use the content. Here’s an example:…”

✍🏻“Write a tweet that explains the core idea behind ChatGPT. Use a similar tone & structure as I do in my regular tweets. But don’t use the content. Here are two examples:…”

0️⃣ Zero Shot

1️⃣ One Shot

🔢 Few Shot

Prompt Rules

Prompt engineering for ChatGPT involves skillfully designing your input prompt to get the desired output from the model. It's a crucial aspect of leveraging GPT models, as it directly impacts the quality of the model's responses. Here are some things to consider:

1. Clarity of Instruction: Make your instruction as clear as possible. Include explicit details about what you want the model to do. If the instruction is too vague, the model might not provide the desired response.

2. Define the Role: If the context is a conversation, specify the role of the model. This will influence the type of language the model uses and how it responds.

3. Setting the Tone and Style: If you want a specific tone or style, mention that in the prompt. For example, if you want the model to generate a response in a formal tone or to mimic a certain author's style, incorporate that into the instruction.

4. Use of Constraints: If there are specific constraints you want to apply (like word limit, avoiding certain topics, or including certain keywords), state these clearly in the instruction.

5. System Message: With chat models, the conversation typically begins with a system message, which sets the behavior of the assistant. It's an important part of the conversation and is often used to set the context.

6. Iterative Refinement: Prompt engineering is often an iterative process. You'll likely need to experiment with different prompts, observe the results, and then refine the prompts based on those observations.

7. User Prompts: Consider the potential variation in user prompts. Users may phrase the same request in different ways. Testing with a broad range of user prompts can help ensure your bot handles this variation effectively.

Remember, even with careful prompt engineering, GPT models might sometimes produce unexpected or undesired responses, and managing these is a part of developing a robust chatbot application.