The prompt paradox: Why your LLM shines during experimentation but fails in production

Have you ever found the perfect prompt that seemed like a prodigy during development, only to witness a disappointing drop in performance when faced with real-world user inputs? This isn’t uncommon. I recently worked with a startup that encountered precisely this issue. Their LLM, initially promising, fell short in production due to a fundamental challenge: Generalization.

Generalization: let’s quickly recap. When asking Gemini what is Generalization, it gives the following answer:

“Generalization is the ability of a machine learning model to apply what it learned from its training data to new, unseen examples. It’s like a student who understands the underlying concepts rather than memorizing the answers. A model that generalizes well can make accurate predictions even in situations it hasn’t encountered before.”

The overfitting trap. Overfitting, the bane of Generalization, occurs when a model becomes too specialized to its training data, missing the broader patterns necessary for success on new data. Think of it as a student who aces a test by rote memorization but struggles with a slightly different set of questions.

You might wonder why this is relevant to prompt engineering. After all, aren’t prompts just simple instructions?

Prompt design impacts generalization

Prompt engineering involves crafting input text (prompts) to guide an LLM’s output. While the model’s inherent ability to generalize is determined during pre-training or fine-tuning, the way we design prompts can significantly impact how effectively that generalization is utilized. Poorly designed prompts can lead to responses that appear overly specific or fail to capture the nuances of a task, hindering the model’s performance in real-world scenarios. On the other hand, well-crafted prompts can help the model better leverage its learned knowledge, eliciting responses that are more generalizable and adaptable to new situations.

Consider the task of sentiment analysis using few-shot prompting. You might provide the LLM with a few examples of movie reviews labeled with their sentiment (positive, negative, neutral). For instance:

Example one:

Review: “This movie was absolutely captivating! 
The acting was superb, and the story kept me on the edge of my seat.”

Sentiment: Positive

Example two:

Review: “I found the plot predictable and the characters underdeveloped. 
A disappointing watch.”

Sentiment: Negative

You could then present an unseen review and ask the model to predict its sentiment based on the provided examples. While this approach might work well for reviews similar in style and content to the examples, the model’s performance might falter when presented with reviews from different genres or written with varying levels of formality, like the unseen review below.

Unseen review:

Revieuw: “This documentary was an eye-opener. It really made me think about the 
complexities of climate change and the impact our actions have on the 
environment. The visuals were stunning, and the interviews with experts 
were informative and thought-provoking. While it was a bit slow-paced 
at times, the overall message was powerful and left a lasting impression.”

This review might not perform well because it’s a different genre, it focuses on the informative aspects, and it has a mixed sentiment. Even seemingly effective prompts can limit generalizability if they overfit or, in this case, narrowly contextualize a task.

Let’s break down the key factors that contribute to this overfitting trap:

• Prompts are too specific: If a prompt is overly tailored to a narrow set of examples, the model may learn to rely on those particular words or phrases rather than the underlying intent.
• The prompt dataset is small. If you test your prompts on only a few examples, assessing how well they generalize to a broader range of inputs is difficult.
• The prompt contains unintended biases: If the prompt includes subtle cues or biases, the model might pick up on those and produce biased outputs, even when given different prompts.

Why Generalization matters in prompt engineering

The concept of Generalization is not only important when training or tuning a model, it’s also important when designing your prompts. Generalization is more than just an academic concern. It’s the key to using LLMs in an adaptable, versatile, and capable way of delivering real value across diverse use cases. We want prompts that:

• Are robust: The prompts work well, even with slight variations in wording or phrasing.
• Can handle new topics: The prompts can be applied to tasks or questions not explicitly seen during development.
• Avoid bias: They don’t produce biased outputs based on unintended cues in the prompt.

Great, Erwin, you are giving us a statistics refresher, but what can we do to prevent this? How can we avoid overfitting when designing prompts? Ok, let’s go back to our use case example and look at some strategies that can help make our prompts more robust, handle new topics, and avoid bias:

1. Embrace diversity in examples

Instead of relying on similar examples, try diversifying the content to expose the model to various styles and nuances.

Example (Original):

Review: “This movie was absolutely captivating! 
The acting was superb, and the story kept me on the edge of my seat.”

Sentiment: Positive

Example (revised — diverse):

Review one: “This movie was an absolute bore. The plot was predictable, 
and the characters were flat.”

Sentiment: Negative

Review two: "While the visuals were stunning, the pacing of the film felt uneven 
and the ending was unsatisfying."

Sentiment: Neutral

Review three: "The concert was electrifying! The band's energy was infectious, 
and the setlist was a perfect mix of old and new hits."

Sentiment: Positive

2. Focus on the task’s intent

Instead of focusing on the specific words in a review, let’s guide the LLM to focus on the intent behind the text, while also making the prompt more adaptable to a variety of inputs and less susceptible to bias.

Example (Original):

Review: “This movie was absolutely captivating!”

Sentiment:

Example (Revised — Intent-Focused):

Text: “[placeholder text about a movie]” 

Instructions: Analyze the text and determine the overall sentiment expressed.
Choose from the following options:
* Positive
* Negative
* Neutral

If the sentiment is unclear or mixed, choose "Mixed".

3. Mitigate bias through neutral phrasing:

To minimize unintentional biases, we can use neutral phrasing and avoid words with strong connotations.

Example (Original):

Review: “This movie was a complete disaster!”

Sentiment:

Example (Revised — Neutral Phrasing):

Review: “I found this movie to be a disappointment.”

How would you describe the reviewer's feelings about the movie?

4. Evaluate using unseen data

Set aside some prompts or examples as a test set to evaluate how well your prompts generalize.

5. Experiment, experiment, and experiment.

Experimentation is crucial for ensuring prompts generalize well because it allows you to evaluate their performance in different scenarios. This iterative process helps uncover weaknesses and biases that can make your prompts more robust and adaptable to unseen scenarios.

The bottom line

Generalization is as relevant in prompt engineering as in traditional machine learning. By being aware of these issues and techniques that help you avoid them, can create more robust, flexible, and fair prompts.

Generalization is as relevant in prompt engineering as in traditional machine learning. By being aware of these issues and avoiding them, you can create more robust, flexible, and fair prompts. For more prompting advice, check out our collection of prompt notebooks in the Google Cloud Generative AI GitHub repository.

A special thanks to Mike Henderson and Karl Weinmeister from Google for their contributions.