When to use prompt engineering vs. fine-tuning

Prompt engineering vs. fine-tuning: Main differences

Key differences between prompt engineering and fine-tuning include the following:

1. Optimization approach. Prompt engineering improves AI outputs by adjusting how users interact with an existing model, whereas fine-tuning alters the model itself by retraining it on new data.
2. Technical complexity. Prompt engineering requires comparatively less technical skill and is easier for end users to implement, whereas fine-tuning requires expertise in machine learning and data management and involves more planning and coordination.
3. Flexibility. Prompt engineering offers immediate, fine-grained control over an AI system's individual outputs, whereas fine-tuning makes more permanent changes to model behavior but is less adaptable in real time.
4. Resource requirements. Prompt engineering demands no new data or computing resources, as it relies solely on human input, whereas fine-tuning requires significant compute and additional high-quality data sets.

Should your organization use prompt engineering or fine-tuning?

Overall, the choice between prompt engineering and fine-tuning depends on an organization's goals, resources and planned use cases.

Prompt engineering is best suited for organizations that need immediate improvements and high adaptability, have limited computational or financial resources, and are confident that model users will be able to write effective prompts.

Fine-tuning is best suited for organizations that need precise, lasting and domain-specific performance improvements and are willing to make the necessary investments in infrastructure, time and technical expertise to get there.

And the choice isn't necessarily either-or. Combining the two approaches can also be beneficial: Many AI teams use prompt engineering to make rapid, flexible adjustments for individual tasks, while reserving fine-tuning runs for deeper, longer-term model changes.

What is prompt engineering?

Prompt engineering optimizes generative AI models' outputs by making strategic changes to user inputs, called prompts. This technique enables prompt experimentation with model outcomes without retraining or additional computational resources.

Because AI models respond directly to the instructions they're provided, the effectiveness of those instructions greatly affects output quality. Good prompt engineering means writing clear, specific and contextualized prompts that lead to more accurate and relevant responses.

Designing prompts effectively typically requires some level of familiarity with the underlying model's structure, capabilities and limitations. Any model user can experiment with prompt engineering techniques, but some organizations also employ dedicated prompt engineers. Full-time prompt engineers typically have a computer science background and use more advanced techniques, like automating prompt experimentation and building organizational prompt libraries.

What is fine-tuning?

Fine-tuning is an optimization technique that shapes the behavior of an existing model using new data sets targeted to specialized tasks or domains. Unlike prompt engineering, fine-tuning directly modifies the actual AI model.

During fine-tuning, the AI model receives raw information from the additional retraining data, which it then uses to adjust its internal parameters. This helps the model produce more accurate, relevant results for the target task -- for example, customer support, legal document summarization or medical diagnostics.

Fine-tuning is better suited than prompt engineering for adapting general-purpose generative AI models to specific business or research use cases. However, it is more resource-intensive and technically demanding. Although fine-tuning can produce substantial improvements in accuracy and contextual relevance, it requires more time, compute resources and data.

Where does retrieval-augmented generation fit in?

Traditional generative AI models respond to users' prompts using only what they learned during training. This both limits their knowledge base -- they can't access recent or privately stored information, for example -- and increases the risk of inaccurate output, known as hallucinations.

Retrieval-augmented generation (RAG) addresses these challenges by enabling models to dynamically access external knowledge sources. RAG-enabled AI systems can query databases and external documents in real time, grounding the AI model's response in verifiable, curated information. In this way, RAG combines the accuracy and specificity of knowledge retrieval with the creativity and flexibility of generative AI models.

RAG vs. fine-tuning vs. prompt engineering

Here's how RAG compares with fine-tuning and prompt engineering:

• RAG. This technique enables generative AI models to retrieve vetted, relevant information from external sources without extensive retraining.
• Fine-tuning. This technique adapts a base generative AI model through additional task-specific training.
• Prompt engineering. This technique guides model responses by changing inputs, rather than the underlying parameters or data sources.

Again, note that these three techniques aren't mutually exclusive -- they each have advantages and can be used to complement each other. It all depends on the project goal, resources at hand and stage of the model lifecycle.

As an example, a health tech company building a chatbot to answer patient questions could start by fine-tuning the base LLM for the medical domain, improving its ability to understand medical terminology and common health inquiries. Next, implementing RAG could improve response accuracy and personalization by enabling the model to access medical databases and health records. Finally, prompt engineers could add suggested questions to the UI that patients see when they log in, helping indicate which query structures are most likely to elicit useful responses.

Editor's note: Stephen J. Bigelow originally wrote this article in August 2023. Lev Craig significantly updated it in March 2025 and added a new section on RAG.

Lev Craig covers AI and machine learning as the site editor for SearchEnterpriseAI. Craig graduated from Harvard University with a bachelor's degree in English and has previously written about enterprise IT, software development and cybersecurity.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.