---
title: "Comparing Prompt Engineering vs Fine-Tuning"
description: "Learn when to use prompt engineering, fine-tuning, or RAG for tasks ranging from simple queries to specialized applications."
date: "2024-11-11"
readTime: "12 min read"
author: "William Bakst"
---

Prompt engineering is about refining and iterating on inputs to get a desired output from a language model, while fine-tuning retrains a model on a specific dataset to get better performance out of it.

Both are ways to improve LLM results, __but they’re very different in terms of approach and level of effort__.

Prompt engineering, which involves creating specific instructions or queries to guide the model's output, is versatile and can be effective for a wide range of tasks, from simple to complex.

It works well for use cases such as summarizing text, generating outlines, and answering general knowledge questions, and it can even handle more sophisticated cases like analyzing legal documents or interpreting medical symptoms when done skillfully.

Fine-tuning, on the other hand, involves further training the model on a specific dataset to specialize its knowledge or capabilities. This approach can be beneficial when dealing with highly specialized domains, unique writing styles, or when consistent performance on a specific task is important.

For instance, fine-tuning might be preferred for creating a model that consistently mimics a company's brand voice or for optimizing performance on tasks requiring deep domain expertise.

To get really specific responses in these areas, it’s hard to get around the need to retrain the model to cope with such specialized tasks.

Knowing when to “engineer prompts” or fine-tune a language model is the topic of this article. We’ll cover the differences between both and discuss their respective pros and cons.

Prompt engineering is in the wheelhouse of our own lightweight prompting toolkit, [Mirascope](https://github.com/mirascope/mirascope), so we’ll also describe some best practices that we’ve incorporated in its design.

Lastly, we describe when to use retrieval augmented generation (RAG), as it’s an increasingly popular option for information retrieval.

## How Prompt Engineering and Model Fine-Tuning Work

Prompt engineering is a relatively low-cost way to get the answers you want without needing to change the attributes of the model, whereas in fine-tuning you’re adapting the model to a new, curated dataset, using a much more resource intensive process. Both involve working with pre-trained models.

### Nudging AI Outputs in the Right Direction

In prompt engineering, __you’re working purely through queries__ sent to the model, and you change those queries as needed until you’re satisfied with the response.

If that sounds iterative and repetitive, it is. Prompt engineering means pouring all your creativity and focus into _how_ you’re asking the question or making the request. And if the LLM doesn’t answer you right, you rephrase the question and ask again.

It’s a continual process of steering the model’s responses to where you want them to end up, by adding context, specifying tone, or emphasizing key information as needed.

When developing LLM-based AI applications, it usually involves a level of [data orchestration](/blog/llm-orchestration) to give the model consistent access to the right context and information flow.

One thing to keep in mind is that the path forward may not be immediately obvious, so refining your prompts along the way is as much a journey of discovery and learning as it is a process of continuous experimentation and adaptation.

Here are a [few types of prompts](/blog/prompt-engineering-examples) (there are many more) for getting the answers you need:

* __Instruction-based prompts__ for telling the model what to do, e.g., “Explain the word ‘optimization’ in simple terms,” or “Recommend top books about generative AI.” Clear instructions keep the model’s output relevant and align them with your expectations.  
* __Zero-shot and few-shot prompts__ provide either no examples (zero shot) or some examples (few shot) to teach the model a new task or show it what you need exactly.  
* __Chain-of-thought prompts__ break down complex tasks into manageable steps, guiding the model (using a single LLM call) to think through each part. This is useful for tasks that require thorough, logical reasoning.  
* __Multi-step prompts__ are a back-and-forth dialog between human and model, where the model asks you clarifying questions for better-informed responses.

### Teaching the Model to Think Differently

In contrast to prompt engineering, fine-tuning adapts the model _itself_ to specific tasks or domains through retraining. This adjusts the model’s parameters to meet specific requirements that __allow it to learn the patterns and nuances of new data__.

Fine-tuning has longer-lasting effects on model behavior than prompting, since it ingrains the model’s responses with a new approach that consistently influences not only the model’s tone and choice of expressions, but also its structure and presentation style.

Fine-tuning involves these general steps:

1. __Select and curate a dataset__ that reflects the new task or domain you want the model to master. The choice and volume of this dataset should be aligned with the size and capabilities of the model being fine-tuned. Larger, more sophisticated models require extensive and diverse datasets, whereas smaller models can get away  with training on less data.  
2. Retrain the model on this dataset (this is known as __the fine-tuning process__), to adjust the model’s internal parameters and weightings to recognize certain contextual signals, linguistic structures, and task-specific attributes. For example, if you want the LLM to write medical research summaries, you’d fine-tune it with thousands of peer-reviewed articles and clinical studies, teaching it the exact tone, terminology, and structure that’s standard in the field.  
3. __Test the fine-tuned model__ against a validation set to ensure it meets your standards. You may need to tweak the training parameters or refine the dataset to address any performance issues along the way.

## Pros and Cons of Prompt Engineering vs Fine-Tuning
  <table style={{ borderCollapse: 'collapse', width: '100%' }}>
    <thead>
      <tr>
        <th style={{ backgroundColor: '#6366f1', color: 'white', padding: '10px', textAlign: 'center', border: '1px solid #6366f1' }}>Feature</th>
        <th style={{ backgroundColor: '#6366f1', color: 'white', padding: '10px', textAlign: 'center', border: '1px solid #6366f1' }}>Prompt Engineering</th>
        <th style={{ backgroundColor: '#6366f1', color: 'white', padding: '10px', textAlign: 'center', border: '1px solid #6366f1' }}>Fine-Tuning</th>
      </tr>
    </thead>
    <tbody>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Barrier to Entry</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Low</strong><br/>Requires minimal technical knowledge and no specialized datasets.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>Requires domain-specific knowledge, a large dataset, and computational resources for training, not to mention a long
  period of training.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Adaptability</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>No retraining is needed, just modify the prompts.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Low</strong><br/>Requires retraining for every new task or domain.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Cost</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Low</strong><br/>Uses pre-trained models without extra computational resources.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>Requires significant computational resources and specialized datasets to adapt pre-trained models.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Scalability</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>Can be applied across different tasks without modifying the model.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Low</strong><br/>Needs retraining for different tasks, which increases resource consumption.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Output Control</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Limited control</strong><br/>You can guide outputs but can't change the model's internal biases.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High control</strong><br/>You can adjust internal parameters to reduce biases and improve performance on specific tasks.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Consistency</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Moderate</strong><br/>May generate inconsistent results, especially with complex tasks -- often requiring multiple iterations.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>Tends to produce more reliable and consistent results, especially for repetitive tasks.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Customization</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Limited</strong><br/>Restricted to adjusting prompts.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>Can be fine-tuned for very specific tasks using specialized data.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Computational Resources</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Low</strong><br/>Requires minimal computational resources.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>Demands significant resources, especially for large-scale models and datasets.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Dataset Requirement</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Low</strong><br/>Works with pre-trained models out-of-the-box.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>High</strong><br/>Requires a large domain-specific dataset for training.</td>
      </tr>
      <tr>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Generalization</strong></td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Good</strong><br/>Generalizes well to different tasks since the pre-trained base model remains unchanged.</td>
        <td style={{ padding: '10px', border: '1px solid #6366f1', verticalAlign: 'top' }}><strong>Might be limited</strong><br/>Risk of overfitting -- the model may become too specialized and lose generalization ability if trained on
  a narrow dataset.</td>
      </tr>
    </tbody>
  </table>

## Best Practices for Prompting and Fine-Tuning

Best practices for prompting are focused on everything around creating effective queries, whereas for fine-tuning they’re concerned with both the data and type of model you’ll be using.

### Strategies for Effective Prompt Engineering

Writing prompts in plain text is sometimes enough to get the answers you want, but to do prompt engineering at scale you’ll need code, since code automates query architecture, manages complex workflows, integrates APIs, and more.

Following best practices promises to keep the whole process running smoothly, and we recommend you approach prompt engineering with the same deliberate and thoughtful mindset as you would any other complex endeavor, such as software development.

Below are listed _some_ of our own core [best practices](/blog/prompt-engineering-best-practices) for advanced prompt engineering:

1. __Write prompts in as clear and contextually rich a way as possible__. Vague prompts are ambiguous and often lack the context necessary to show the direction you want the model to take in its responses.  
2. [__Colocate everything__](/blog/engineers-should-handle-prompting-llms) __that affects the quality of an LLM call with the call itself__. This means grouping model type, temperature, and other parameters altogether (along with placing the prompt in the near vicinity), as opposed to scattering these around the codebase. This was actually a big sticking point for us when first using the OpenAI SDK and [LangChain](/blog/langchain-sucks), which didn’t seem to care about keeping everything together, and was one of the reasons we designed [Mirascope](/docs/mirascope).  
3. Following on from the previous point, [__version and manage__](/blog/prompt-versioning) __everything that you’ve colocated together, as a single unit__. We can’t stress this enough - this makes it easy to track changes and roll back to previous versions. We even have a [dedicated tool for automatically versioning and tracing](/docs/lilypad) for easier prompt management.  
4. Prioritize validating LLM outputs and __use advanced retry mechanisms to handle errors effectively__. Apply tools like [Tenacity](https://tenacity.readthedocs.io/en/latest/) to automate retries, reinserting errors into subsequent prompts, allowing the model to refine its output with each attempt. [Mirascope provides utilities](/docs/v1/learn/retries/#error-reinsertion) to ease this process by collecting validation errors and reusing them contextually in calls that follow, improving model performance without you needing to make manual adjustments.

### Guidelines for Fine-Tuning

Fine-tuning is a different beast from prompt engineering because here you’re not just adapting inputs.

Rather, you’re reshaping the model’s behavior, so you want to ensure you’re not overfitting its responses to the new training data or introducing unintended biases.

Below are some best practices for fine-tuning language models:

* __Choose a pre-trained model that aligns well with your target task.__ This should be obvious, but it’s important to focus on the foundational capabilities of the base model rather than on immediate performance gains. For example, choosing a model that was trained on a dataset similar to your use case means a less costly and time-consuming adaptation phase.  
* __Ensure your dataset is clean, well-formatted, and relevant__ to the target domain, since high-quality data reduces the likelihood of overfitting or biasing the model. Also, use a dataset that’s large enough to capture the nuances of the domain but not so large that it introduces noise. A balanced dataset helps the model to generalize better.  
* __Fine-tune your model’s settings__ - like learning rate, batch size, and epoch - to optimize the training process for the specific task. Start with small values for the learning rate to prevent drastic updates that could lead to model instability, and adjust based on validation performance. Alternatively, if you have sufficient computational resources, you can run a hyperparameter optimization job that tries out different combinations of these values and selects the best configuration based on validation performance. Choose a batch size that balances computational efficiency with model accuracy, and experiment with different numbers of training epochs to avoid both underfitting and overfitting.  
* Instead of training the model all at once, __fine-tune it in small, iterative steps__ to assess the impact of each update and to avoid drops in performance. Continuously evaluate the model’s accuracy using validation sets. This helps you to spot overfitting, ensure consistency in [structured outputs](/blog/langchain-structured-output), and decide if more data or further fine-tuning is worthwhile.

## Choosing Prompt Engineering vs Fine-Tuning vs RAG

Deciding between prompt engineering and fine-tuning LLMs isn't strictly an _either/or_ proposition since combining both gives good results.

A good place to start is with a baseline - a reference point that measures initial model performance on a specific task - using a well-engineered prompt, to provide a benchmark for comparison before applying additional techniques like fine-tuning or RAG.

If you can get good results with a few-shot prompt, for example, there may be no need to apply the more complex and resource-intensive process of fine-tuning. You can simply leverage the model’s existing capabilities directly through its API, which is (of course) cost effective.

### Using Retrieval Augmented Generation

With RAG, you’re beefing up prompts with added context that’s extracted from a knowledge base that’s easily updated. This knowledge base is often indexed using [embeddings](/blog/llm-pipeline), which capture the semantic meaning of the information, making it easier to retrieve relevant context for the query.

And so RAG might be a better choice for tasks needing external, up-to-date information that go beyond the natural language model’s generic training.

Examples of scenarios where RAG is useful include:

* __Answering questions that require the latest information__, such as trends in financial markets, current events, or recent scientific discoveries. For instance, a medical assistant could pull patient information to provide responses more tailored to individual patients.  
* __Providing insights from a specialized repository__, like legal databases or academic research papers. For instance, a legal assistant [tool](/blog/llm-tools) could use RAG to pull relevant case law and legal precedents from a database of thousands of court rulings.

## Use Mirascope to Unlock the Full Potential of Prompt Engineering

Mirascope empowers developers to create, manage, and scale sophisticated prompts by providing modular building blocks rather than locking them into a [framework](/blog/prompt-flow-vs-langchain).

Our lightweight toolkit lets users experiment, iterate, and deploy [effective prompts](/blog/llm-prompt) using the Python they already know, and slots readily into existing developer workflows, making it easy to get started.

Want to learn more? You can find in-depth Mirascope code samples on both our [docs](/docs/mirascope) and on [GitHub](https://github.com/mirascope/mirascope/).
