LLM Fine-Tuning Guide for Enterprises in 2025
Follow the links for the specific solutions to your LLM output challenges. If your LLM:
- Doesn’t have access to the facts needed in your domain, either train a new LLM, switch to a domain-specific one or use RAG to retrieve facts
- Has relevant facts but needs to answer in a different style and tone, follow certain output formats, or use certain tools, then:
- First leverage prompt engineering or prompt chaining to improve results
- If they don’t work, LLM fine-tuning is the right approach. You can use your LLM provider’s service or fine tune open source LLMs on-prem.
The widespread adoption of large language models (LLMs) has improved our ability to process human language. However, their generic training often results in suboptimal performance for specific tasks.
To overcome this limitation, fine-tuning methods (See Figure 1) are employed to tailor LLMs to the unique requirements of different application areas.
Figure 1: Search volume for “llm fine tuning” according to Google Trends in August 2025.
What is LLM fine-tuning?
Fine-tuning a large language model adjusts a pre-trained model to perform specific tasks or to cater to a particular domain more effectively. The process involves training the model further on a smaller, targeted dataset that is relevant to the desired task or subject matter.
The original large language model is pre-trained on vast amounts of diverse text data, which helps it to learn general language understanding, grammar, and context. Fine-tuning leverages this general knowledge and refines the model to achieve better performance and understanding in a specific domain.
Figure 2: Capabilities of an LLM after fine-tuning.1
For example, a large language model might be fine-tuned for tasks like sentiment analysis in product reviews, predicting stock prices based on financial news, or identifying symptoms of diseases in medical texts.
This process customizes the model’s behavior, allowing it to generate more accurate and contextually relevant outputs for tasks such as:
- Sentiment analysis.
- Chatbot development.
- Question answering.
How to fine-tune LLMs
1. Preparing the dataset
Since LLMs are pre-trained on a fixed dataset, they are not aware of real-time events. To keep these models up-to-date and improve their performance on specific, evolving topics, businesses use real-time web data. This data is critical for two main reasons: it helps with domain alignment and reduces hallucination.
- Domain alignment and relevance: Using web-sourced data allows companies to fine-tune LLMs on the most current and relevant information for their industry. For example, a legal tech company could use web crawlers to collect recent court rulings and legal blogs. This domain-specific data ensures the fine-tuned model understands up-to-date terminology and industry context, which is often missing from static, publicly available datasets. This process is key to making a general-purpose pre-trained model an expert in a specific field.
- Reducing hallucination: Hallucination occurs when an LLM generates plausible but factually incorrect information. By fine-tuning an LLM with high-quality, real-world data from the web, you provide it with a reliable source of truth. This makes the model less likely to invent information during inference and helps it generate more accurate and trustworthy responses. This process ensures the model’s outputs are grounded in reality rather than fabricated content.
Businesses either use in-house web scraping tools or third-party providers to gather data from websites. This collected training data is then prepared and used to fine-tune the LLM.
By continuously incorporating fresh web data, businesses can ensure their fine-tuned models remain relevant and accurate, providing a significant competitive advantage.
OpenAI states that each doubling of the dataset size leads to a linear increase in model quality.2
2. Choosing a foundation model and a fine-tuning method
Selecting the appropriate base model and fine-tuning method depends on the specific task and data available. There are various LLM providers to choose from, including OpenAI, Alphabet, and Meta, each with its own strengths and weaknesses. The fine-tuning method can also vary based on the task and data, such as transfer learning, sequential fine-tuning, or task-specific fine-tuning.
While choosing the base model, you should consider:
- Whether the technical infrastructure is suitable for the computing power required for fine-tuning
- Whether the model fits your specific task
- Input and output size of the model
- Your dataset size
3. Fine-tuning
Fine-tuning as a service for closed-source models
Most LLMs (e.g. OpenAI’s GPT-3.5 and GPT-4, Google Gemini, Cohere) offer fine-tuning services.3 Anthropic partnered with Amazon Bedrock for finetuning.4
Pricing of fine-tuning depends on the model and the tokens used. Prices tend to be a few dollars per million tokens for the default level of fine-tuning (i.e., 4 epochs).5
Fine-tuning open source models
Since the weights of the model are available in open source models, enterprises can fine-tune open source models on-prem without exposing their datasets to LLM providers.
Steps to fine-tune open source models include:
Loading the pre-trained model: Once the LLM and fine-tuning method have been selected, the pre-trained model needs to be loaded into memory.
This step initializes the model’s weights based on the pre-trained values, which speeds up the fine-tuning process and ensures that the model has already learned general language understanding.
Fine-tuning involves training the pre-trained LLM on the task-specific dataset. The training process involves optimizing the model’s weights and parameters to minimize the loss function and improve its performance on the task.
The fine-tuning process may involve several rounds of training on the training set, validation on the validation set, and hyperparameter tuning to optimize the model’s performance.
For example, Llama models can be fine-tuned economically with Parameter Efficient Fine Tuning (PEFT) approaches.6
Enterprises can leverage their MLOps or LLMOps platforms to fine-tune models.
4. Evaluating fine-tuned models
Once the fine-tuning process is complete, the model’s performance needs to be evaluated on the test set. This step helps to ensure that the model is generalizing well to new data and is performing well on the specific task. Common metrics used for evaluation include accuracy, precision, recall, and F1 score.
5. Deployment
Once the fine-tuned model is evaluated, it can be deployed to production environments. The deployment process may involve integrating the model into a larger system, setting up the necessary infrastructure, and monitoring the model’s performance in real-world scenarios.
What are the methods used in the fine tuning process of LLMs?
Fine-tuning methods
Fine-tuning is a process that involves adapting a pre-trained model to a specific task or domain by training it further on a smaller, task-specific dataset. Several fine-tuning methods can be used to adjust a pre-trained model’s weights and parameters to improve its performance on the target task:
- Transfer learning involves reusing a pre-trained model’s weights and architecture for a new task or domain. The pre-trained model is usually trained on a large, general dataset, and the transfer learning approach allows for efficient and effective adaptation to specific tasks or domains.
- Sequential fine-tuning: The pre-trained model is fine-tuned on multiple related tasks or domains sequentially. This allows the model to learn more nuanced and complex language patterns across different tasks, leading to better generalization and performance.
- Task-specific fine-tuning: The pre-trained model is fine-tuned on a specific task or domain using a task-specific dataset. This method requires more data and time than transfer learning but can result in higher performance on the specific task.
- Multi-task learning: The pre-trained model is fine-tuned on multiple tasks simultaneously. This approach enables the model to learn and leverage the shared representations across different tasks, leading to better generalization and performance.
- Adapter Training involves training lightweight modules that are plugged into the pre-trained model, allowing for fine-tuning on a specific task without affecting the original model’s performance on other tasks.
Few-shot learning method
Few-shot learning (FSL) involves improving model performance without changing model weights. In this approach, the model is provided with a limited number of examples (i.e., “few shots”) from the new task, and it uses this information to adapt and perform better on that task. It can be considered as a
- Lower-cost alternative to fine-tuning. The only cost is the input tokens for a few examples.
- Meta-learning problem where the model learns how to learn to solve the given problem.
Figure 3: Few-shot learning scenario where the model learns to classify a set of images from the tasks it was trained on.7
This is particularly useful when there’s not enough data available for traditional supervised learning. In the context of LLMs, fine-tuning with a small dataset related to the new task is an example of few-shot learning.
Differences between few-shot learning & fine-tuning
The primary difference is the amount of task-specific data required for the model to adapt to a new task or domain. Fine-tuning methods require a moderate amount of task-specific data to optimize the model’s performance, while few-shot learning methods can adapt models to new tasks or domains with only a few labeled examples.
What are some fine-tuning examples?
Fine-tuning achieved significant performance increases in finance
For example, Bloomberg has developed BloombergGPT, a large-scale language model tailored for the financial industry. This model focuses on financial natural language processing tasks such as sentiment analysis, named entity recognition, and news classification.
The BloombergGPT was created using a combination of finance and general-purpose datasets, and led to high scores in benchmark tests (Figure 4).
Figure 4: Image showing how BloombergGPT performs across two broad categories of NLP tasks: finance-specific and general-purpose.8
Why or when does your business need a fine-tuned LLM?
Businesses may need fine-tuned large language models for several reasons, depending on their specific requirements, industry, and objectives. Here are some common reasons:
1. Customization
Businesses often have unique needs and goals that a generic language model may not address. Fine-tuning enables them to tailor the model’s behavior to suit their specific objectives, such as generating personalized marketing content or understanding user-generated content on their platform.
Discover how fine-tuning LLMs enables the creation of customized products and marketing strategies, ultimately enhancing the generative AI experience in retail, marketing, and insurance.
2. Data sensitivity and compliance
Businesses handling sensitive data or operating under strict regulatory environments might need to fine-tune the model to ensure it respects privacy requirements, adheres to content guidelines, and generates appropriate responses that comply with industry regulations.
3. Domain-specific language
Many industries use jargon, technical terms, and specialized vocabulary that may not be well-represented in the general training data of a large language model. Fine-tuning the model on domain-specific data allows it to understand and generate accurate responses within the context of the business’s industry.
4. Enhanced performance
Fine-tuning improves the model’s performance on specific tasks or applications relevant to the business, such as:
- Sentiment analysis
- Document classification
- Information extraction
This can lead to better decision-making, higher efficiency, and improved outcomes.
5. Enabling agentic AI capabilities
Fine-tuning is critical for developing agentic AI systems, which are designed to act autonomously, make decisions, and interact with external tools or environments to achieve specific goals.
By finetuning an LLM, businesses can enhance their ability to perform function calling, enabling the model to select and execute appropriate tools (e.g., APIs, databases) with accurate parameters.
For example, a finetuned LLM can power an agentic AI that autonomously manages customer inquiries by integrating with a CRM system or retrieves real-time data via web APIs. This customization ensures the model understands domain-specific contexts and tool interactions, making agentic AI more effective and reliable in enterprise applications.
6. Improved user experience
A fine-tuned model can offer a better user experience by generating more accurate, relevant, and context-aware responses, leading to increased customer satisfaction, in applications like:
- Chatbots
- Virtual assistants
- Customer support systems
What is a large language model (LLM)?
A large language model is an advanced artificial intelligence (AI) system, more specifically an enterprise generative AI model, designed to process, understand, and generate human-like text based on massive amounts of data. These models are typically built using deep learning techniques, such as neural networks. They are trained on extensive datasets that include text from a broad range, such as books and websites, for natural language processing.
One of the key aspects of a large language model is its ability to understand context and generate coherent, relevant responses based on the input provided. The size of the model, in terms of the number of parameters and layers, allows it to capture intricate relationships and patterns within the text. This enables it to perform various tasks, such as:
- Answering questions
- Text generation
- Summarizing text
- Translation
- Creative writing
Prominent examples of large language models include OpenAI’s GPT (Generative Pre-trained Transformer) series, with GPT-3 and GPT-4 being the latest iterations.
Foundation models, like large language models, are a core component of AI research and applications. They provide a basis for building more specialized, fine-tuned models for specific tasks or domains.
Figure 5: Examples of foundation models.9
Further reading
- While fine-tuning improves the efficacy of large language models, it’s essential to address the Risks of Generative AI.
- Fine-tuning large language models comes with legal considerations. Explore the legal landscape surrounding these advanced AI systems in generative AI in legal or gen AI copyright.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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