Striking the Right Balance of Examples in Prompts for AI Assistants

Examples are a powerful prompt component. Well-chosen examples guide AI systems by demonstrating the desired response format. However, example balance is critical – too few and the AI lacks sufficient specification, too many and prompts become bloated and ineffective. In this post, I’ll explore tips for striking the right balance with examples in prompts.

As an expert in prompt engineering for AI assistants, I help clients fine-tune example use as part of comprehensive prompt optimization. We’ll dig into the nuances of combining just enough examples to maximize prompt power. Mastering example balance takes practice – let’s break it down together!

Why Examples Matter in Prompts

First, a quick recap on why prompt examples deserve attention. Examples help prompts by:

• Illustrating the ideal response clearly
• Establishing the level of detail needed
• Introducing relevant facts and concepts
• Activating the AI’s learned knowledge
• Shaping the style and tone of output
• Mitigating undesired bias risk

Without any examples, prompts can be too ambiguous. But excessive examples create confusion. So balancing example inclusion is imperative.

Finding the Goldilocks Zone of Prompt Examples

I think of effective example balance as finding the “Goldilocks Zone” – avoiding too many or too few examples by striking that sweet spot in between.

Too few examples leave your prompt underspecified and generic. But too many make your prompt overconstrained and bloated. Well-balanced examples walk that fine line to provide the AI just enough specification without going overboard.

Finding your Goldilocks example balance takes trial and error. There are no hard rules – it depends on the AI system, use case, and desired response. But the guidelines in this post will get you on the right track.

Assessing Under-Specification from Too Few Examples

How can you recognize when a prompt doesn’t have enough examples? Some signs include:

• Responses parrot back prompt components verbatim
• The AI generates generically applicable content
• Tone and style are not customized per the intent
• There is repetition of the few examples provided
• Responses lack sufficient length or detail
  
  

Under-specified prompts fail to provide the clarity needed for highly tailored outputs. Adding more tailored examples addresses these issues.

Spotting Over-Specification from Excessive Examples

On the other hand, how do you know when too many examples bog down prompt effectiveness? Common symptoms include:

• High prompt complexity slows response generation
• Examples conflict with each other in tone or content
• Responses mash together disjointed fragments from examples
• The AI struggles to balance and prioritize all examples
• Responses become jumbled and incoherent
  
  

Pruning back excessive examples restores prompt concision and coherence.

Key Principles for Balancing Prompt Examples

With the goal of finding that sweet spot between under and over-specification, here are some key principles for example balance:

• Default to 1-3 examples for most prompts
• Adjust based on response quality and speed
• Favor relevance over quantity
• Prioritize illustrative clarity
• Align examples to user intent and context
• Limit example length when possible
• Test compressing overlong prompts
• Ensure examples pin down style and tone
• Avoid tensions between competing examples
• Drop inessential examples that don’t add value

Applying these principles will help calibrate your example inclusion.

Prompt Example Template Framework

I recommend starting from this simple example template framework:

Input – Main question or instructions

1-3 Relevant Examples – Concise, tailored examples demonstrating ideal response

Suffix – Any final reinforcement of instructions

Then expand, reduce or refine examples as needed based on response testing.

Adjusting Example Balance for Different AI Systems

The ideal example balance also varies across AI systems based on model size and training data:

• Smaller models may need more examples for sufficient specification
• Narrow expert models can overfit complex examples
• Large general models like GPT-3 perform well with minimal examples
• Examples matching training data are most effective

Learn each model’s “Goldilocks Zone” through iterative testing.

Prompt Example Balance Examples

Let’s look at some examples balancing specification and concision:

Help Request

How can I politely ask my neighbor to keep the noise down at night? Please provide a sample script with constructive phrases I could use when talking to them.

This has just one highly relevant example guiding tone and content.

Email Drafting

Please write a draft email scheduling a meeting with a client next week. Include 2-3 sample sentences I could use when proposing meeting agenda items and availability.

The request for 2-3 example sentences provides focused specification.

Argument Summary

Can you summarize the key arguments in this research paper? Here are two high-level example arguments it makes: “This paper argues machine learning techniques can generate misleading results if not carefully monitored.” “The authors advocate for greater transparency in AI to build public trust.”

Two concise examples illustrate the desired summary style.

Refining Example Balance Through Testing

Dialing in the right example balance is an iterative process. Leverage response testing to refine example quantity and content including:

• Removing inessential examples that don’t improve responses
• Pruning lengthy or redundant examples
• Expanding with additional examples if responses seem generic
• Revising example wording to better match the desired tone and style
• Sampling alternative example combinations to identify an optimal set
  
  

Like any prompt component, examples require ongoing optimization. But balancing concise, tailored examples in your prompts hugely shapes the AI’s response – so this tuning pays dividends in better AI performance.

I hope these tips help you leverage examples more effectively in your prompts. Please reach out if you need any consulting support optimizing your approach to prompt examples. Striking the right balance takes practice, but it’s worth the effort. Well-specified prompts make all the difference!