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Provide Examples (Few-Shot Learning)

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one-shot/two-shot/Few-shot/many-shot learning is a powerful technique in prompt engineering that involves providing the AI model with examples of desired interactions. This method helps guide the model to understand and replicate the expected behavior in similar situations. You have probably seen this used in LLM benchmarking when ranking models.

Using examples in your prompts can significantly improve the AI’s understanding of the task and lead to more accurate and consistent responses. Read more here

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Key Benefits and Implementation

Improved Output Quality

Provide examples that demonstrate the exact format and content you expect. This leads to more accurate and relevant responses from the AI.

Consistency

Show multiple examples to establish patterns that the AI can follow consistently across different interactions.

Task Comprehension

Use examples to illustrate complex or nuanced tasks, making them clearer for the AI to understand and execute.

Safety and Control

Demonstrate how to handle sensitive topics or avoid unwanted content through carefully crafted examples.

Example Formats:

While examples are powerful, be careful not to overload your prompt with too many. Start with one or two examples and add more only if necessary to achieve the desired output.

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Reinforcing instructions

Repeat important instructions throughout the prompt to ensure the AI maintains consistent behavior.

Reinforcing key instructions helps prevent the AI from “forgetting” important guidelines as the conversation progresses. This is crucial for maintaining consistency in longer interactions.

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Implementation and Best Practices:

Identify & Prioritize

Review your prompt and identify the most critical instructions or the ones that the model has difficulty following.

Strategic Placement

Decide where to repeat key instructions within your prompt. Common placements include the beginning, after providing context, and at the end as a final reminder.

Natural Language & Visual Cues

Integrate reinforced instructions using natural, conversational language. Use formatting techniques so the instructions are distinct and easily noticeable for the model.

Test and Refine

Regularly test your prompts and adjust the reinforcement strategy based on performance. Ensure a balance between reinforcing key points and maintaining overall clarity in your instructions.

While reinforcement is important, be careful not to over-complicate your prompt with excessive repetition.

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Example prompts

These prompts utliziew different markup languages and techniques like CoT, Few-shot, reinforcement and setting boundaries:


### Role
You are a customer support agent for an ecommerce wine store.

### Task
Assist customers in selecting the perfect wine based on their preferences and requirements.

### Reinforcement
- Always suggest at least three different wines.
- Highlight the key features of each wine, such as taste, origin, and price.
- Offer a satisfaction guarantee and easy return policy to reassure customers.

### Chain of Thought
1. Greet the customer and inquire about their wine preferences.
2. Ask if they have any specific occasion or food pairing in mind.
3. Based on their responses, recommend three wines that fit their criteria.
- Remember: Always suggest at least three different wines.
4. Provide detailed descriptions for each recommended wine.
- Remember: Highlight the key features of each wine, such as taste, origin, and price.
5. Reiterate the satisfaction guarantee and return policy.
6. Close the conversation by asking if they need any further assistance.

### Remember!
- You are a support agent for an ecommerce wine store.
- Always suggest at least three different wines.
- Highlight the key features of each wine, such as taste, origin, and price.
- Offer a satisfaction guarantee and easy return policy to reassure customers.
Step 3: Implement Chain of Thought ReasoningStep 5: Advanced Techniques and testing your prompts