Prompt Engineering vs Fine Tuning: When to Use Each

When building AI applications using large language models(LLMs), developers face a crucial decision in the fine-tuning vs prompt engineering debate. Choose LLM fine-tuning when you need specialized domain knowledge, have training data available, and require consistent outputs at scale. Choose prompt engineering for quick prototyping, multiple tasks with one model, or when you lack training data and computational resources. This guide will help you understand the key differences and make the right choice for your specific use case.

Fine-tuning vs prompt engineering: key differences

Fine-tuning and prompt engineering help us adapt a general-purpose LLM to a specific use case. However, they significantly differ in approach, cost, complexity, and use case. Here is a quick comparison of LLM fine-tuning vs prompt engineering:

Now that we’ve covered fine-tuning vs prompt engineering basics, let’s explore what LLM fine-tuning and prompt engineering are, their advantages and disadvantages, and how they compare in detail.

What is LLM fine-tuning?

LLM fine-tuning is the process of retraining a general-purpose LLM on a specific dataset to adapt its working to a specialized task or domain. Open-source LLMs are trained on massive web-scale datasets and work well for general tasks that do not require task-specific or domain-specific knowledge. To make the LLMs useful for domain-specific or task-specific use cases, we create specific datasets and retrain the models using the following process:

In LLM fine-tuning, we first prepare a custom labeled dataset to train the model. Using the curated dataset, we fine-tune a general-purpose LLM and save it. The user interacts directly with the fine-tuned LLM during inference to get the output. For example, we will use the following steps to fine-tune an LLM for a text summarization task.

1. First, we will create a dataset with the source text and its expected summary output.
2. Next, we will retrain a general-purpose LLM using the dataset.
3. After fine-tuning, the LLM will learn the patterns and specific details of summarizing a text.
4. We will save the fine-tuned model and use it directly to summarize new input text.

Thus, the fine-tuning process gives us a new LLM with different characteristics from the original general-purpose model.

What is prompt engineering?

Prompt engineering is the process of designing and refining inputs to the LLMs that can help us achieve more accurate and controlled outputs. Unlike fine-tuning, where we retrain the LLM using new datasets to get better results, prompt engineering decides how to ask the model to get better results without retraining the original model.

In prompt engineering, we first think through the desired output from the LLM. Then, we give a prompt to a general-purpose LLM and analyze its output. After analyzing the output, we refine the prompt and pass it to the LLM until we get the output in the desired format. After this, we save the refined prompt as the system prompt, which is used during inference.

For example, if we want to build a text summarization app using LLMs, we can try the following prompts:

Prompt template 1:

Summarize this text. 
--- Actual text to summarize---  

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Prompt template 2:

Summarize the following text in 3-5 bullet points, focusing on the main arguments and key takeaways. Avoid filler and keep the tone neutral. 
--- Actual text to summarize---  

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Prompt template 3:

You are a text summarization assistant. Summarize the following text in 3-5 bullet points, focusing on the main arguments and key takeaways. Avoid filler and keep the tone neutral. 
--- Actual text to summarize---  

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Copy to clipboard

After trying the different prompts, we select the prompt that gives the best output and use it as the system prompt for the text summarization app. The system prompt is appended to the actual text when the user sends a text to summarize in real time. The combined system prompt and text are sent to the LLM to generate the summary.

With this background on fine-tuning and prompt engineering, let’s discuss their advantages and disadvantages.

Advantages and disadvantages of LLM fine-tuning

LLM fine-tuning has the following advantages:

• Domain and task specialization: LLM fine-tuning helps us to adapt a general-purpose open-source model for a specific domain or task. For example, you can take a general-purpose model like llama3.3, retrain it using medical literature, and use it specifically for providing prescriptions and medical advice. Similarly, you can retrain gemma 3 on a custom chat style to create a chatbot. Fine-tuning helps us get precise and relevant responses for specific use cases beyond the capabilities of an open-source model.
• Low latency during inference: Using methods like retrieval-augmented generation (RAG) or prompt engineering for specialization introduces latency while generating responses. This is because data retrieval steps and increased context length of the prompts increase the time required to generate the final output. Fine-tuned LLMs avoid this latency.
• Controlled LLM outputs: Training LLMs using an open-source web dataset introduces bias due to the inherent bias in the source data. Fine-tuning LLMs with reinforcement learning using human feedback helps us improve the LLM outputs by aligning the model responses to avoid toxic language, sensitive topics, and legal risks.
• Increased accuracy for smaller models: By fine-tuning smaller LLMs for specific tasks, we can save on compute and deployment costs while keeping the model performance intact. For example, a fine-tuned Gemma3 4B model can match the performance of an open-source Gemma3 27B model on a domain-specific question-answering task.

Along with these advantages, LLM fine-tuning also has some limitations:

• High training cost: LLM fine-tuning requires expensive hardware, skilled data scientists, and a lot of time. Small startups and individuals might be unable to afford the resources needed to fine-tune large language models.
• Data preparation: The fine-tuned LLM will be only as good as the training dataset. Hence, we need to prepare datasets devoid of any inconsistency or noise that can reduce performance, cause hallucination, or introduce bias to the model. Curating a balanced dataset is a time-consuming and expensive process.
• Reduced adaptability: To adapt a fine-tuned model to new datasets, we need to repeat the data preparation and retraining process, which is a resource-intensive task. This leads to slower iteration cycles for model updates. Thus, LLM fine-tuning isn’t suitable for fast-changing environments with frequent data updates.
• Catastrophic forgetting: LLMs can forget previously learned knowledge while fine-tuning as the model weights get overwritten. This leads to a loss of general reasoning ability, resulting in poor performance outside the fine-tuned domain.
• Limited transferability: Fine-tuned LLMs are less suitable for multi-task systems. An LLM fine-tuned on medical literature won’t work well for legal tasks. Similarly, a model fine-tuned for document summarization might not work well for chat-based tasks.

Now that we know the advantages and disadvantages of LLM fine-tuning, let’s discuss the same for prompt engineering so that we can choose the better approach.

Advantages and disadvantages of prompt engineering

Prompt engineering has the following advantages:

• Improved outputs: Correctly engineered prompts help us get relevant and accurate responses with correct tone, formality, and length. With techniques like chain-of-thought prompting, we can also help the LLMs generate accurate responses for multi-step tasks, all using open-source LLMs.
• Low costs: Prompt engineering doesn’t require data infrastructure like memory, GPUs, or locally hosted LLMs. We can use it even while using LLMs through ChatGPT, Gemini, or Claude APIs. We don’t even need to prepare datasets for prompt engineering, resulting in a very low capital and resource requirement.
• Flexibility: Prompt engineering allows us to quickly modify prompts and check the model outputs. This allows us to adapt the same large language model to perform different tasks or change outputs without lengthy training cycles or data processing steps.
• No technical knowledge required: You can use prompt engineering even if you have no idea how LLMs work. You can modify prompts iteratively and evaluate outputs to get tailored, high-quality results using prompt engineering.

Along with these advantages, prompt engineering also has some limitations:

• Limited control: Prompt engineering doesn’t change the underlying knowledge base or reasoning capabilities of the LLM. We can only change the prompts to get the desired response. If the LLM’s original training data doesn’t include information about a specific domain or task, no amount of prompt engineering can get us the correct output.
• Consistency issues: Small changes in wording, punctuation, or phrasing in a prompt can drastically affect the output quality. If a prompt works well in one context, it might fail to produce better results in a different context. Similarly, if a prompt works well with a particular LLM, it might not work well with another model.
• Context length limits: Every LLM has a finite token limit or context window that decides how much information can be provided to or retained by the LLM in a single interaction. Long prompt templates reduce the space for query, input data, and output as they take up a part of the LLM’s context window. Hence, we must provide relevant and compact prompts that help the models generate output without exceeding the token limit.
• Trial and error costs: Prompt engineering requires many iterations where we change the prompts and evaluate the LLM output. Suppose you are using LLMs through API from providers like OpenAI or Anthropic. In that case, each trial incurs an additional cost, which may accumulate significantly over time.

Now that we know the advantages and disadvantages of prompt engineering and fine-tuning, let’s discuss the use cases where LLM fine-tuning is better than prompt engineering.

When to choose LLM fine-tuning over prompt engineering?

In the fine-tuning vs prompt engineering debate, fine-tuning is preferred in many applications due to better control over the outputs and low latency during inference. The following are some of the use cases where LLM fine-tuning works better than prompt engineering:

• Pattern-based tasks: Fine-tuning is better than prompt engineering for large-scale classification tasks. LLMs can learn patterns from historical data for tasks like email classification, report generation, spam detection, or support ticket tagging. Then, they can use the learned patterns in real time for the given task.
• Regulated domains: Prompt engineering doesn’t give us complete control over the model output. We should use fine-tuning instead of prompt engineering while working in high-risk and regulated domains like law, finance, and healthcare.
• Complex multi-step reasoning tasks: Prompt engineering isn’t foolproof for complex multi-step tasks. If you want to use LLMs for tasks that require multi-step reasoning, you should use fine-tuning for better accuracy.
• Inference cost and latency: Large prompts increase the response time of an LLM, which makes prompt engineering unsuitable for large-scale production systems. If you are building systems at scale that require minimum latency, you should use fine-tuned LLMs. Large prompts also increase the memory usage at runtime. Due to this, inference costs increase if you have hosted your LLMs on cloud platforms that charge based on usage. Hence, it is better to use fine-tuned LLMs for low latency and inference costs.
• Brand-specific tone: When building chatbots and customer service agents using LLMs, it is important to ensure that the chatbot or agent doesn’t say anything that damages the company’s brand image. Racist or harmful responses can also lead to reputation or business loss for the company. We can fine-tune LLMs using reinforcement learning with human feedback to align them to brand values and tone.

After discussing these use cases, let’s discuss cases where prompt engineering works better than LLM fine-tuning.

When is prompt engineering better than LLM fine-tuning?

When comparing prompt engineering vs fine-tuning, prompt engineering is more suitable for fast-paced teams with a low compute budget. The following are some of the use cases where prompt engineering is more suitable than LLM fine-tuning.

• Fast iteration and prototyping: LLM fine-tuning takes a lot of time in data preparation and model training. If you are building a prototype or minimum viable product, you can use prompt engineering to adapt LLMs for your tasks quickly.
• No training data available: Fine-tuning requires a curated and labeled dataset. If the dataset is not available for your use case, you can use prompt engineering, using instructions and examples to guide the model in generating the required outputs.
• Using a single model for multiple tasks: Fine-tuned models become better for specific use cases. However, they might not perform well for general tasks. If you plan to use a single LLM for multiple tasks and domains, prompt engineering should be the preferred approach.
• Low compute budget: If you are an individual developer or a small startup who cannot afford GPUs and data infrastructure, you can access LLMs from providers like OpenAI and Gemini using APIs and use prompt engineering to adapt the LLMs for your use case. This will help you save costs while building LLM-based applications.

While LLM fine-tuning and prompt engineering work differently, their goal is to get better outputs from an LLM. We can also combine fine-tuning and prompt engineering to get better results. Let’s discuss how to do so.

Combining LLM fine-tuning with prompt engineering

Combining prompt engineering and fine-tuning is often a better strategy for achieving high accuracy and flexibility when using LLMs in real-world applications.

For example, you have fine-tuned an LLM using your company’s proprietary datasets. Now, your company’s HR manager and accountant have different needs and need significantly different responses from the LLM. Here, you can use prompt engineering to ensure that the LLM answers accurately to the accountant and the HR manager by adding role-specific prompts for each stakeholder.

Thus, you can use LLM fine-tuning to help the model learn domain or task specific knowledge. Then, you can use prompt engineering to decide the output’s tone, style, and format based on the user persona.

Conclusion

Both fine-tuning and prompt engineering help us get high-quality and consistent outputs from LLMs. While LLM fine-tuning helps us specialize LLMs for domain or task specific use cases, it also comes with higher costs and complexity. Prompt engineering provides a lightweight, flexible, and cost-effective approach to control the LLM behavior without changing its parameters, which makes it suitable for rapid experimentation and multi-task adaptability. Understanding how these techniques work will help you strategically combine them to achieve optimal performance of your generative AI applications.

To learn more about prompt engineering, you can take this learn prompt engineering course that discusses effective prompting techniques to craft high-quality prompts, maximizing your use of generative AI. You might also like this fine-tuning transformer models course that discusses LLM fine-tuning with techniques like LoRA and QLoRA.

Frequently asked questions

1. How is prompt engineering different from fine-tuning in the context of LLMs?

LLM fine-tuning changes its internal weights. Prompt engineering decides how an input is provided to the LLM, and it doesn’t impact the model’s properties.

2. What is the difference between context and prompt in LLM?

Prompt is the specific input text we give to the LLM model to generate a response. It includes the query, instructions, examples, or other information we pass to the model. Context is the entire information the model considers for generating the response. It includes the prompt, the previous conversation history, and system messages in the current session memory.

3. What is the difference between fine-tuning and alignment in LLM?

Fine-tuning involves retraining an LLM on a new dataset to adapt it to a particular task or domain. Alignment involves adjusting the model’s behavior to generate safe, ethical, and brand-specific outputs. We can align LLM’s behavior using reinforcement learning or prompt engineering.

4. What are the types of fine-tuning?

There are four main types of fine-tuning for large language models:

• Full fine-tuning: Updates all model parameters using your training data. Provides maximum customization but requires significant computational resources.
• LoRA (Low-Rank Adaptation): Fine-tunes only small adapter layers while keeping the original model frozen. Reduces training time and memory usage by up to 90%.
• QLoRA (Quantized LoRA): Combines LoRA with model quantization to further reduce memory requirements. Enables fine-tuning on consumer-grade GPUs.
• Prefix tuning: Adds trainable prefix tokens to the input sequence while keeping model parameters frozen. Fastest approach with minimal resource requirements.

Choose full fine-tuning for maximum performance, LoRA for balanced efficiency, QLoRA for limited hardware, or prefix tuning for quick experimentation.

5. Which is better, RAG or fine-tuning?

RAG is better when you need up-to-date information, have document collections but no training data, or want to cite sources. Fine-tuning is better when you have labeled training data, need consistent domain-specific responses, or require low-latency inference.

Choose RAG if you:

• Need real-time or frequently updated information.
• Have knowledge bases or document collections.
• Want to provide source citations.
• Have limited training data.

Choose fine-tuning if you:

• Have quality labeled training datasets.
• Need consistent, specialized responses.
• Require fast inference with minimal latency.
• Want complete control over model behavior.

For optimal results, combine both approaches: use fine-tuning for domain adaptation and RAG for knowledge enhancement.