“If you can’t measure it, you can’t improve it.” - Peter Drucker

Leveraging Machine Learning Engineering to Solve Prompt Engineering

In the rapidly evolving landscape of artificial intelligence, large language models like GPT-3 have become indispensable tools for tasks ranging from text generation to question answering. However, unlocking their full potential often hinges on a process known as prompt engineering — the art of designing the perfect input (or “prompt”) to elicit the desired output from these models. While effective, manual prompt engineering is time-consuming, expertise-dependent, and difficult to scale. Fortunately, we can address these challenges by applying the principles of machine learning engineering, transforming prompt engineering into a systematic, data-driven process. In this blog post, we’ll explore how to use machine learning engineering to solve prompt engineering, breaking it down into familiar steps: requirements gathering, problem formulation, and optimization.

What Is Prompt Engineering?

Before diving into the solution, let’s define the problem. Prompt engineering involves crafting input text to guide a language model toward producing accurate, relevant, or contextually appropriate outputs. For example, to get a model to summarize a paragraph, you might experiment with prompts like:

  • “Summarize this text: [text]”
  • “Provide a concise summary of the following: [text]”
  • “In a few sentences, summarize: [text]”

The goal is to find the phrasing that yields the best result. However, this trial-and-error approach doesn’t scale well as tasks multiply, and it relies heavily on human intuition. Machine learning engineering offers a more structured alternative.

Solving Problems with Machine Learning Engineering

Machine learning engineering provides a proven framework for tackling complex problems. The process typically involves:

  1. Requirements Gathering: Understanding the problem’s context, including the business objective, features, data availability, constraints, scale, and performance requirements.
  2. Problem Formulation: Defining the problem as a machine learning task, which at its core is an optimization problem requiring an objective function.
  3. Modeling and Optimization: Selecting a modeling approach and an optimization algorithm to maximize or minimize the objective function.

We can adapt this workflow to solve prompt engineering, treating the search for the optimal prompt as a machine learning optimization challenge. Let’s walk through each step.

Step 1: Requirements Gathering

The first step is to gather the requirements for prompt engineering, aligning them with the needs of the task at hand.

  • Business Objective: The goal is to obtain the most effective output from the language model for a given task—whether it’s answering a question, generating a summary, or translating text. For instance, if the task is question answering, the objective might be to maximize factual accuracy.
  • Features: In this context, the “features” are the prompts themselves—the various ways we can phrase or structure the input to the model.
  • Data Availability: We need data to evaluate prompt performance. This could involve a dataset of input-output pairs (e.g., questions and correct answers) or a mechanism to score outputs (e.g., human evaluation or automated metrics like BLEU for text generation).
  • Constraints: Practical limits might include prompt length, computational resources, or the need to adhere to specific formats (e.g., natural language vs. structured templates).
  • Scale: The solution should handle a single task or extend to many tasks, such as generating prompts for hundreds of questions.
  • Performance Requirements: We need a clear metric for success—accuracy for classification tasks, fluency for generation tasks, or a custom scoring function tailored to the application.

With these requirements defined, we can move to formulating the problem.

Step 2: Formulating Prompt Engineering as a Machine Learning Problem

At its essence, machine learning is about optimization—finding the inputs or parameters that maximize (or minimize) an objective function. For prompt engineering, the problem becomes: find the prompt that maximizes the quality of the model’s output for a specific task.

Mathematically, let’s denote:

  • ( M ): The language model (a fixed, pre-trained system).
  • ( x ): The prompt (the input we’re designing).
  • ( M(x) ): The output generated by the model given prompt ( x ).
  • ( S ): A scoring function that measures how well ( M(x) ) accomplishes the task (e.g., accuracy, relevance, or coherence).

Our goal is to find the optimal prompt ( x^* ):

[ x^* = \arg\max_x S(M(x)) ]

This casts prompt engineering as a black-box optimization problem. The function ( f(x) = S(M(x)) ) is “black-box” because we can’t directly analyze the internals of ( M ) (the language model); we can only evaluate its outputs. The challenge lies in efficiently searching the vast space of possible prompts—essentially all possible strings—to find ( x^* ).

Step 3: Modeling and Optimization

With the problem framed as optimization, we need a modeling approach and an algorithm to solve it. Since the space of prompts is complex (discrete text) and evaluating ( S(M(x)) ) can be costly (e.g., involving model inference or human scoring), we can explore several practical methods:

Approach 1: Continuous Prompt Tuning

Instead of searching over discrete words or phrases, we can parameterize the prompt as a set of continuous embeddings—vectors in the model’s input space. These embeddings act as a “soft prompt” that can be prepended to the input. Because they’re continuous, we can optimize them using gradient-based techniques like stochastic gradient descent:

  1. Initialize a set of trainable embedding vectors.
  2. Pass them through the model along with the task input.
  3. Compute ( S(M(x)) ) based on the output.
  4. Update the embeddings via backpropagation to maximize ( S ).

This approach, known as prompt tuning, leverages the differentiability of modern language models and has been shown to outperform hand-crafted prompts in many cases.

For cases where we want human-readable prompts, we can treat ( x ) as a discrete string and use search-based optimization methods:

  • Bayesian Optimization: Model the relationship between prompts and scores, iteratively testing promising candidates.
  • Genetic Algorithms: Evolve a population of prompts by mutating and combining high-scoring ones.
  • Random Search: Sample and evaluate a variety of prompts, refining based on results.

For example, we might start with templates like “Answer this question: [question]” and optimize by adding instructions (e.g., “Answer accurately and concisely: [question]”) or examples.

Approach 3: Few-Shot Example Selection

Many language models excel with few-shot prompting, where the prompt includes examples of the task (e.g., question-answer pairs) before the target input. Here, optimization involves selecting the best subset of examples from a dataset to include in the prompt. We could:

  • Use similarity metrics to pick examples close to the target input.
  • Apply diversity measures to cover a range of cases.
  • Train a separate model to predict which examples maximize performance.

This reduces the search space and leverages the model’s ability to generalize from demonstrations.

Benefits of This Approach

Applying machine learning engineering to prompt engineering yields significant advantages:

  • Efficiency: Automates the trial-and-error process, reducing manual effort.
  • Scalability: Handles large numbers of tasks or inputs systematically.
  • Performance: Finds prompts that outperform human intuition by optimizing directly on task metrics.
  • Flexibility: Adapts to new tasks through data-driven optimization.

Challenges to Consider

Despite its promise, this method isn’t without hurdles:

  • Data Needs: Effective optimization often requires a dataset of examples or a reliable scoring function, which may not always be available.
  • Computational Cost: Repeatedly querying the language model and optimizing prompts can be resource-intensive.
  • Generalization: Prompts optimized for specific tasks may not transfer well to others, necessitating task-specific tuning.

Conclusion

Prompt engineering is a linchpin in the effective use of language models, but its manual nature limits its scalability and consistency. By adopting a machine learning engineering approach—gathering requirements, formulating the problem as optimization, and applying techniques like continuous prompt tuning or discrete search—we can revolutionize how we interact with these models. This shift not only streamlines the process but also enhances performance, paving the way for more robust and adaptable AI systems. As language models continue to grow in capability, integrating machine learning engineering into prompt engineering will be key to unlocking their full potential.

References

@article{
    leehanchung,
    author = {Lee, Hanchung},
    title = {Statistics for AI/ML, Part 3: Measuring Classification Agreements Between Agents},
    year = {2025},
    month = {03},
    howpublished = {\url{https://leehanchung.github.io}},
    url = {https://leehanchung.github.io/blogs/2025/03/03/cohen-kappa/}
}