Prompt Engineering vs. Fine-Tuning: How to Choose the Right Approach for Your Needs

Introduction

Large language models (LLMs) are ubiquitous these days. They power services like Google Search, note-taking applications like Notion, assistants like Alexa and Siri, and even our code editors.

LLMs are trained on a vast corpus of data, making them extremely knowledgeable and helpful to their users in various topics. For example, you can log in to ChatGPT and talk to it about almost anything, such as politics, sports, marketing, and more. However, these models struggle when asked about domain or company-specific data. They either fail to answer or produce factually incorrect responses.

Depending on your goals, constraints, and available data, you can tailor an LLM using one of three primary techniques:

• Prompt Engineering: a lightweight approach that leverages the model’s existing knowledge by crafting input prompts to shape the output.
• Fine-Tuning: a more resource-intensive method that trains (or retrains) the model on a task-specific dataset to teach it new skills or domain expertise.
• Retrieval-Augmented Generation (RAG): a hybrid technique that connects an LLM to external information sources or databases, giving it a means to “look up” facts dynamically (this article will not cover RAG in detail).

In this article, we will focus on prompt engineering and fine-tuning. We begin with a side-by-side comparison to give you a high-level overview of their differences. Then, we dive deeper into each approach, explaining how it works, how to use it effectively, and highlighting their key use cases. Finally, we outline the scenarios where each approach is most suitable, helping you decide which one aligns best with your specific needs.

Here’s what you’ll find:

• Side-by-Side Comparison: Prompt Engineering vs. Fine-Tuning
• Prompt Engineering
  • How Prompt Engineering Works
  • Why Prompt Engineering?
  • Common Prompt Engineering Techniques

4. Fine-Tuning
   • How Fine-Tuning Works
   • Popular Fine-Tuning Tools
5. When To Use Prompt Engineering And Fine-Tuning
   • When to Use Prompt Engineering
   • When to Use Fine-Tuning

Side-by-Side Comparison: Prompt Engineering vs. Fine-Tuning

	Prompt Engineering	Fine-Tuning
Definition	Modifies or carefully crafts the input prompt to guide a pre-trained model’s output without altering the model’s parameters.	Adjusts the model’s internal parameters by training on domain-specific or task-specific data; effectively “teaches” the model additional knowledge or adaptation.
Ease of Implementation	Very easy to get started; you primarily need access to a capable model and the skills to write effective prompts.	Moderate to difficult: Requires data collection, labeling, model architecture familiarity, and hyperparameter tuning. Setting up the environment (GPU/TPU) and training pipelines can be time-consuming.
Costs	- Low additional cost: Typically you only pay for model usage (API tokens) or a subscription (e.g., ChatGPT Plus). - No specialized hardware required for further training.	- Higher cost: Requires computational resources (powerful GPUs/TPUs), data engineering, and more extensive development cycles. - Data labeling can also be expensive, especially if domain experts are needed. - Long-term, can pay off for mission-critical tasks.
Flexibility	- High: The same pre-trained model can be prompted to perform numerous tasks (summarization, classification, brainstorming, etc.) without retraining. - Good for rapid prototyping and pivoting across different domains.	- Task-Specific: A fine-tuned model is optimized for certain tasks or domains; using it for very different tasks can degrade performance or require additional fine-tuning.
Performance Ceiling	- May struggle with highly specialized or deeply technical tasks if the base model lacks the necessary domain knowledge. - Prone to inconsistency; small changes in prompt wording can yield different results.	- Often superior performance in specialized or complex tasks once properly trained. - Better handles domain-specific terminology and compliance/regulatory constraints (e.g., legal, medical).
Reliance on Pre-Trained Knowledge	- Fully depends on what the base model has already learned; cannot introduce genuinely new concepts or significant domain knowledge.	- Can expand the base model’s knowledge by training on additional data; effectively injects new vocabulary, topics, or style.
Resource Intensity	- Minimal: Prompt crafting is mostly a matter of trial, error, and creativity; no heavy compute or large-scale data processing is required.	- High: Fine-tuning large models can be extremely GPU/TPU-intensive and requires a robust MLOps pipeline for data prep, training, and validation.
Typical Use Cases	- Quick experiments with new tasks (e.g., generating outlines, exploring ideas). - Low-stakes scenarios where perfect accuracy is not critical or where domain knowledge is general.	- High-stakes scenarios (healthcare, legal, finance) where accuracy and domain alignment are essential. - Complex tasks requiring advanced model understanding or specialized language.

Prompt Engineering

Prompt Engineering focuses on carefully designing input prompts to guide generative AI tools like ChatGPT, Midjourney, or Gemini in generating more relevant, accurate, and task-specific outputs. When interacting with a generative AI model via an API, this process is also referred to as prompt engineering.

How Prompt Engineering Works

Prompt engineering works by adapting the model's behavior without altering its underlying parameters. Instead, it leverages the model's pre-trained knowledge or, in other words, what the model already knows, to achieve the desired outcomes.

Why Prompt Engineering?

Prompt engineering is easy to implement as it does not require deep technical expertise in the model's architecture or training process. It's also resource-light, and enables users to achieve results quickly.

Here's an example of how prompt quality impacts results:

Simple Prompt

When you first prompt the model, the response that you get back may not meet the desired quality. For instance, when you prompt ChatGPT to write a short email for festival leave, the model will respond with something like this:

Refined Prompt: Adding Details

The output is far from what you would include in the actual email to your manager. Let’s refine it. Instead of simply sending “Write a short email for festival leave” as the prompt, let’s add a few more details:

You can see the model response below:

The new response is more organized and has all the details.

By providing clearer instructions and details, you guide the model toward producing output that is closer to what you actually need.

Common Prompt Engineering Techniques

Let’s examine some key prompt engineering techniques you can use to craft effective prompts and adapt the model you’re using.

Role Prompting

In role prompting, instruct the model to take on a specific role or persona, such as a teacher, a food critic, or a customer service representative. This helps generate output that aligns with the characteristics of that role.

As an example, if you ask the model to generate a review for a pizza place, it will generate something generic:

Instead, if you instruct the model to assume the role of a food critic and write a review for the pizza place, you will get a better response:

Instruction Prompting

Instruction prompting takes advantage of the LLM’s ability to follow natural language instructions. It aims to allow models to work with new, previously unseen tasks by understanding and executing instructions expressed in natural language without requiring task-specific training data.

The example below uses instruction prompting to mask the user's private data from the text.

Few-Shot Prompting

LLMs are trained on a huge corpus of data. As a result, LLMs develop a broad set of skills and pattern recognition abilities. LLMs can use these abilities at inference time to rapidly adapt to or recognize the desired task and perform whatever is requested. This innate ability of LLM to quickly adopt to newer situations is called in-context learning.

Few-Shot prompting includes examples in the prompt to evoke LLM’s in-context learning abilities. Few-Shot prompting includes examples in the prompt to improve the quality and accuracy of LLM’s response. This reduces the need for curating labelled datasets and an expensive fine-tuning process.

Let’s say you want the model to classify a text as positive, negative, or neutral. Instead of fine-tuning, you can add a few example input-output pairs and the model will help you classify the sentiment.

By showing the model a pattern, you enable it to generalize and handle new inputs better.

Fine-Tuning

While prompt engineering is fast and accessible, it has limitations. For complex tasks or those requiring deep domain-specific knowledge, the outputs of a general-purpose LLM may fall short. This is because the model's performance is inherently constrained by its existing training data and the quality of the input prompts. Additionally, model's token limits, units of text like words or characters, may prevent you from passing all your data to the LLM through the context.

In such cases, the best option is to fine-tune the model using your custom dataset. Fine-tuning modifies the model parameters such that the model trains to perform well on domain-specific tasks.

Compared to prompt engineering, fine-tuning is more resource-intensive and requires technical knowledge, including:

• Optimizer configurations and hyperparameter tuning.
• Preparing a high-quality, domain-specific dataset.
• Understanding the model's architecture and pre-training setup.

How Fine-Tuning Works

Fine-tuning adapts a pre-trained LLM to perform well on specific tasks by re-training it with a carefully curated dataset. Here’s an example to illustrate its application:

Let’s say you own an app that records medical conversations and stores them as text files. Now, you want to extract the names of procedures and medicines mentioned during the conversation. Since the names of medicines are a niche subject, general-purpose LLMs may not be able to extract them, and since the LLM itself might lack medical knowledge, prompt engineering may not help. In such cases, you can fine-tune the model on your custom dataset of labeled medical text can significantly improve its accuracy for this task.

Fine-tuning involves three key steps:

1. Data collection and preparation

The first step is to gather a high-quality labeled dataset. This can include examples where the desired output is explicitly linked to the input. For instance, your dataset might contain medical transcripts with annotations marking procedure names and medication references.

However, labeling data for fine-tuning can be resource-intensive and costly, especially for domain-specific tasks. Once the dataset is collected, you will need to convert it into a format suitable for the LLM to consume. Typically, this involves tokenizing, a process of translating text into tokens, numerical representations the model understands. You can use HuggingFace Tokenizers, an open-source library of today's most used tokenizers.

2. Hyperparameter adjustments

Before starting the training process, you can configure the model’s hyperparameters to optimize performance. For example:

• Add new layers to specialize the model for your task.
• Freeze existing layers to retain the general knowledge the model has already learned, thereby speeding up the fine-tuning process.

3. Training

In the final step, the model is fine-tuned using the curated dataset. Unlike pre-training, which involves billions of parameters and massive datasets, fine-tuning typically works with smaller datasets and requires less time. The goal is to refine the model’s behavior for specific use cases without overriding its foundational knowledge.

Popular Fine-Tuning Tools

Fine-tuning can be expensive and technically challenging, but several tools simplify the process. Here are some popular solutions:

• Low-Rank Adaptation of Large Language Models (LoRA): Developed by Microsoft, LoRA focuses on fine-tuning a select subset of the model’s parameters. This approach reduces computational overhead while maintaining strong performance. It’s particularly useful when resources are limited.

• LLaMA-Factory: LlamaFactory is a unified framework that integrates a suite of cutting-edge, efficient training methods. It features an intuitive web UI called LlamaBoard that allows users to customize the fine-tuning of 100+ LLMs without requiring significant coding expertise.

• HuggingFace: HuggingFace is a widely used open-source library that provides access to pre-trained models, datasets, and tools for fine-tuning. It also eases the training process by providing tokenizers, optimizers, and other tools in the same library. Moreover, it is compatible with popular deep learning frameworks like Pytorch and TensorFlow, making it highly accessible.

When To Use Prompt Engineering And Fine-Tuning

In summary, prompt engineering is best when you have limited labeled data or constrained resources (like time or budget). It’s also ideal for quick iteration across multiple tasks without investing in new model versions.

In contrast, fine-tuning is well-suited for scenarios demanding maximum accuracy, domain-specific knowledge, or stringent compliance. If you have sufficient data and budget, fine-tuning can yield substantial performance gains and faster inference.

When to Use Prompt Engineering

Scenario	Reason
You have limited labeled data	Few-Shot or Chain-of-Thought (CoT) prompting can enhance performance using only minimal examples embedded in the prompt, thereby sidestepping the need for large labeled datasets.
Resources are constrained (time, computational power, or budget)	Prompting typically requires no additional training, avoiding the computational, time, and budget overhead of fine-tuning. This is especially beneficial for smaller teams or those with limited GPU/TPU resources.
You require rapid prototyping for multiple tasks	A single base model can be prompted differently for diverse tasks (e.g., summarization, grammar correction, classification). This setup offers quick experimentation without maintaining multiple fine-tuned models.
You want minimal engineering overhead	Prompt-based solutions can be quickly deployed, with changes easily made to the prompt rather than retraining or re-deploying a model. This is ideal for fast, iterative development cycles.

When to Use Fine-Tuning

Scenario	Reason
You need high accuracy for domain-specific applications	Fine-tuning on domain-specific data (with unique vocabularies or regulatory guidelines) can significantly boost accuracy and reliability. In regulated fields like healthcare or finance, specialized language usage is often critical.
You want to optimize performance for complex or highly specialized tasks	When prompt engineering alone is insufficient, fine-tuning provides deeper adaptation to the target task’s data distribution. This can yield more precise outputs, reduced hallucinations, and better alignment with domain knowledge.
You operate in highly regulated industries (e.g., healthcare, finance)	Through targeted fine-tuning, organizations can incorporate industry-specific compliance requirements into the model. This helps maintain strict standards (e.g., HIPAA in healthcare or PCI DSS in finance).
You need real-time application performance	Fine-tuned models can be smaller or more optimized, often resulting in faster inference and reduced latency compared to large, generic base models, beneficial for time-critical use cases (e.g., real-time chatbots or interactive analytics).
You have sufficient labeled data and training resources	Fine-tuning becomes cost-effective and practical when enough high-quality training data is available—especially if the long-term returns (improved accuracy and reliability) outweigh the initial investment in computation and ML engineering.

Conclusion

Both prompt engineering and fine-tuning offer distinct advantages for adapting large language models to specialized needs. For quick or general tasks like brainstorming ideas or automating simple text generation, prompt engineering is typically a faster and more cost-effective option. It draws on the model’s existing knowledge and allows you to achieve reasonable accuracy with minimal setup.

By contrast, fine-tuning is better suited for highly specialized or regulated domains (e.g., healthcare, law, finance), where even minor inaccuracies can have serious consequences. Though more resource-intensive, fine-tuning can teach the model new vocabulary, domain-specific rules, and compliance requirements, ultimately delivering higher accuracy and consistency.

Bhuwan Bhatt

Bhuwan Bhatt, a Machine Learning Engineer with over 5 years of industry experience, is passionate about solving complex challenges at the intersection of machine learning and Python programming. Bhuwan has contributed his expertise to leading companies, driving innovation in AI/ML projects. Beyond his professional endeavors, Bhuwan is deeply committed to sharing his knowledge and experiences with others in the field. He firmly believes in continuous improvement, striving to grow by 1% each day in both his technical skills and personal development.

Valeriia Kuka

Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.