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 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 (Figure 1). However, their generic training often results in suboptimal performance for specific tasks. To overcome this limitation, fine-tuning methods are employed to tailor LLMs to the unique requirements of different application areas.

What is LLM fine tuning?

Fine-tuning a large language 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: Capabilities of an LLM after the fine tuning

Benefits of LLM fine tuning

Source: AssemblyAI

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 the tasks like:

• Sentiment analysis
• Chatbot development
• Question answering

How to fine-tune LLMs

1- Preparing the dataset

Since LLMs remain static, businesses can rely on real-time web data to continuously keep them up to date. Incorporating real-world web data can improve domain alignment and reduce hallucination during inference.

In addition to using prepackaged or synthetic datasets, many teams incorporate domain-specific web-sourced data to enrich their fine-tuning corpora. Collecting data directly from websites, whether through in-house pipelines or via third-party providers, ensures access to up-to-date terminology and industry context that static public datasets may lack.

OpenAI states that each doubling of the dataset size leads to a linear increase in model quality.¹

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, 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.² Anthropic partnered with Amazon Bedrock for finetuning.³

Pricing of fine-tuning depends on the model and the tokens used. Prices tend to be a few dollar per million tokens for the default level of fine-tuning (i.e. 4 epochs).⁴

Fine-tuning open source models

Since the weights of the model is 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.⁵

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: Few-shot learning scenario where the model learns to classify a set of images from the tasks it was trained on

Source: Borealis AI

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

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 7).

Figure: How BloombergGPT performs across two broad categories of NLP tasks: finance-specific and general-purpose

Source: Bloomberg

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 may not be addressed by a generic language model. 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.

Learn how fine-tuning LLMs can deliver customized products and marketing strategies, improving generative AI in Retail, marketing or insurance experience.

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.

Domain specific language can streamline developing SEO strategies with Generative AI for every business.

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- 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, and 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: Foundation models 

Source: Madrona News

Further reading

• Uncover the various methods employed in fine-tuning large language models with a focus on real-world examples: Large Language Models Examples.
• 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.

External Links

• 1. OpenAI Platform.
• 2. OpenAI Platform.
• 3. “Fine-tune Claude 3 Haiku in Amazon Bedrock”. Anthropic.
• 4. Pricing | OpenAI.
• 5. Fine-tuning | How-to guides.