How to Fine-Tune Generative AI Models for Industry-Specific Accuracy and Use Case Control

To fine-tune a generative AI model means adapting a powerful general-purpose system to perform accurately within a specific industry or use case. This process involves injecting domain knowledge, regulatory requirements, and best practices into the model so it can deliver more relevant, compliant, and high-quality outputs. Fine-tuning is essential for optimizing customer experience, increasing operational efficiency, and improving accuracy in tasks like document analysis or personalized communication. Unlike prompt engineering, which adjusts the way users interact with the model, fine-tuning reshapes the model’s internal parameters based on curated, domain-specific datasets—turning a general AI into a specialized, high-performance solution.

Foundation models are initially trained on vast, general-purpose datasets sourced from diverse online content. Advanced models like Google’s Gemini (formerly Bard) leverage extensive internet-scale data. Fine-tuning adapts these models to specific industries by retraining them on smaller, domain-specific datasets, adjusting their internal weights and parameters to improve relevance, accuracy, and performance in targeted applications.

The need for domain adaptation and business control has become more urgent as generative AI adoption has grown. Fine-tuning large language models (LLMs) with domain-specific data enhances their performance on specialized tasks. Research indicates that domain-adaptive pretraining leads to significant performance gains across various domains and tasks. For instance, a study by Gururangan et al. (2020) demonstrated that continuing pretraining on domain-specific corpora improves model accuracy on downstream tasks. Similarly, Databricks highlights that continued pretraining and fine-tuning can enhance a model’s performance, making it more effective for specific applications. These findings underscore the importance of tailoring LLMs to specific domains to achieve optimal results.

The customization processes required to expand LLM accuracy, improve relevancy, and maximize utility of LLM models typically run on a spectrum between prompt engineering and fine-tuning. Simple customizations where a company needs to train LLMs on strict and concise instructions can be handled through prompt engineering. Prompt engineering requires less effort, time, and expenses to set up and iterate during model development compared to parameter or weight training and adjustment.

Mobilizing to process the vast pools of unstructured data that they own is one of the major use cases of fine-tuning. Extra parameters designed to extract generative AI knowledge from this data help drive performance and accuracy over time. Models can perform better by refining those frozen parameters or weights of pre-trained models. They reduce hallucinations by augmenting them with explicit knowledge from training datasets. Fine-tuning additionally adds functionality by allowing direct modifications to models. These posterior or additional functionalities allow processed models to scale across various organizational functions.

What Is Fine-Tuning in Generative AI?

Fine-tuning in generative AI is the process of further training a deep learning model that was already fully trained on a much larger general dataset. Fine-tuning adjusts model weights and connections using a smaller, domain-specific training dataset that is especially relevant to the intended use case. It secures better input/output alignment, more relevant and accurate output, and much greater control over the resulting generative model’s behavior. As generative AI grows more central to business functions, questions like “what is fine tuning in ai” will only become more relevant to business managers.

This diagram illustrates where fine-tuning fits within the LLM stack and lifecycle, as well as some common related tools and methods.

Fine-tuning essentially “rewires” a smaller section of a foundation model’s neural network to make it better at identifying patterns and connections for a specific use case. This improves the performance of a model for a specific industry, domain, or task beyond what is possible with the foundation model alone and probably even beyond what could be reached with prompt engineering.

Why Fine-Tune a Generative AI Model?

The business reasons why fine-tune an AI model include enhancing accuracy for customer support, legal, and research use cases, improving compliance, adaptation to specific tones for brand identity, providing expertise through domain-specific data, and adapting to new patterns or scenarios. Fine-tuning generative AI models is required because a zero-shot prompt only memorizes and retrieves information. Fine-tuning leverages task-specific data to enable generalization and recognition of patterns.

It is more computationally efficient than training a model from scratch and is more efficient in terms of data. Fine-tuning circumvents copyright and privacy issues. Prompts can periodically surface inappropriate examples (known as prompt leaking), but fine-tuning models limit data exposure. Prompt engineering is useful when rapid iteration is required and often provides a short-term hack. It is hard to scale, not always reproducible, or robust. Prompt-based retrieval augmentation has more room for error than fine-tuning.

Accuracy Improvement

Fine-tuning AI models with domain-specific data has proven effective in enhancing customer support metrics. For example, implementing generative AI chatbots and knowledge management systems has led to a 10–15% increase in first-call resolution and over a 20% reduction in average handle time. In the realm of language translation, fine-tuning large language models with domain-specific data has improved translation accuracy, particularly for low-resource languages like Swahili. Techniques such as Retrieval-Augmented Generation have significantly enhanced the quality of Swahili conversational AI systems, demonstrating the value of fine-tuning in both customer support and language translation applications.

Tone & Style Adaptation

Some clients desire a language model assistant to convey a specific language tone or set of idioms to always communicate brand identity or to always frame compliance instructions in a specific legal style. This is not possible with prompts and only fine-tuning with existing text archives will allow it.

Domain-Specific Responses

Businesses use fine-tuning to ensure AI understands and generates responses that are relevant and precise to their target market, instead of using generic responses, by feeding it multiple domain-specific datasets. This improves customer engagement and reduces ambiguity.

Regulation Compliance

When generative AI models are fine-tuned with domain- or jurisdiction-specific regulatory data, it is easier for AI systems to ensure they stay within regulatory boundaries for language and logic. This is particularly important for firms operating in the financial services sector.

Pretraining vs Fine-Tuning vs Prompt Engineering

Pretraining, fine-tuning, and prompt engineering are all methods for shaping a generative AI model’s output. Each has tradeoffs in terms of flexibility, cost, speed, and governance control. Understanding them will help businesses allocate resources optimally while achieving their model development goals.

Pretraining

Pretraining builds the foundation of a model’s general knowledge. Transformers, the deep learning architecture most LLMs use, are initialized with parameters. These are then trained by inputting large volumes of unlabeled data. This data consists of books, articles, or code. The model decodes relationships between input and output using a language modeling objective.

This pretraining takes weeks or months on clusters of thousands of powerful GPUs. Once the model has a basic understanding of general language patterns, pretraining is stopped, and fine-tuning begins. Pretraining provides flexibility but is extremely costly and time-consuming.

Fine-tuning

Fine-tuning customizes the pretrained model for greater accuracy on specific data, context, and domain knowledge. This step is carried out with carefully curated, supervised datasets containing labeled data specific to the task at hand, often encoded in domain-specific knowledge base articles, documentation, or technical manuals.

Fine-tuning refines the parameters of the pretrained model without disrupting the general knowledge. As a result, fine-tuned models can adapt quickly to new situations by customizing the general foundation with specific domain knowledge. Fine-tuning vs prompt engineering for speed and efficiency is a common comparison, with fine-tuning somewhat slower but still far more streamlined than pretraining.

Prompt Engineering

Prompt engineering provides a simple, low-cost way to control the behavior of a large language model without changing its parameters. Prompt engineering is often described as zero-shot, few-shot, or negative-context learning. In zero-shot learning, the model is given only a single prompt to predict a new output. In Few-shot learning, the model is given a small number of prompts and corresponding outputs to predict new outputs. In negative-context learning, the model is given examples of what not to do in the prompt.

Prompt engineering enables fast customization and governance. For example, prompts can be written for a model that instructs it to remain within certain security, compliance, or regulatory boundaries. Similar instructions could be passed through fine-tuning, but it would be much more costly and time-consuming but have greater long-term control, using less compute resources.

Types of Fine-Tuning Techniques

The main types of AI fine-tuning include full fine-tuning, few-shot learning, LoRA (Low-rank Adaptation), QLoRA (Quantized LoRA), and parameter-efficient tuning.

Full fine-tuning updates a model’s parameters using new, labeled data. Few-shot learning uses a small set of samples to adapt a pre-trained model, reducing computational load. LoRA adapts a large model with fewer parameters via low-rank approximation. QLoRA is like LoRA but uses lower-precision computations to further reduce computational needs. Parameter-efficient tuning adapts a model by updating only a small subset crucial to a new task.

Factors to consider when choosing the best type of fine-tuning technique for AI include the dataset size, available computational resources, time constraints, and the specific level of control or accuracy needed. Each method offers a distinct strategy, so selecting the most appropriate one depends on the unique requirements and limitations of the particular case in which the model is being used.

Full fine-tuning is sometimes advantageous when a large, high-quality labeled dataset and significant computational resources are available, making it preferable to LoRA. While LoRA and QLoRA can be quicker and more resource-efficient, there may be situations where full fine-tuning provides better accuracy and control. Fine-tuning can also reduce concerns about model hallucination on specific use cases, as shown in this infographic by OpenAI.

What is parameter-efficient fine-tuning (PEFT)?

Parameter efficiency tuning (PEFT) refers to fine-tuning large pre-trained models by updating only a small, designated subset of the model’s parameters.

This approach has grown in popularity during the last few years as an efficient and practical solution for adapting large foundation models (FMs) to specific downstream tasks. PEFT enables developers to train a far smaller number of tunable parameters and achieve similar accuracy to full parameter fine-tuning, reducing training time and memory usage while adapting FMs to new tasks.

Parameter-Efficient Fine-Tuning (PEFT) methods are strategies designed to adapt large pre-trained models to specific tasks efficiently by updating only a subset of parameters. The primary PEFT methods include prompt-based methods (such as prompt tuning and prefix tuning), adapter-based methods (which insert small trainable layers within the model), and LoRA (Low-Rank Adaptation, which introduces trainable low-rank matrices into the model’s layers). While these methods are well-documented and widely used, specific statistics regarding their adoption rates or representation in evaluation suites are not publicly available.

Methods such as Adapters, which require inserting an additional layer, are easy to implement, highly versatile, and modular as they enable adapter-specific tasks. LoRA, which inserts trainable ranks between attention layers, and its variant DS-LoRA, dynamically frees up GPU memory and allow larger tasks to be handled without significant memory requirements. Prompt-based methods are efficient with respect to memory and storage requirements.

When to use few-shot learning instead of full fine-tuning?

Few-shot learning should be used instead of full fine-tuning for minor or incremental model adaptability. This is best when the new tasks are closely related to what the foundational model already knows. It is also better for use cases where you want to test a new workflow or data domain but do not want to invest heavily in full fine-tuning yet.

In situations where adaptability, speed, lower cost, and minimal data samples are prioritized, few-shot learning is the best approach. If resource requirements such as time, money, and computing resources are not a barrier and the model underperforms on new tasks and domains, full fine-tuning may be more appropriate.

Ultimately, full fine-tuning is necessary when the tasks are substantially different from the base model (domain adaptation). Resource and time requirements should also be considered.

Step-by-Step Fine-Tuning Process

The fine-tuning process for generative AI models requires deliberate steps and best practices. It should include the following seven steps;

1. Problem and Goals Identification: Clearly define the task, the expected outputs (accuracy, fluency, compliance, etc.), and the end user’s needs:
2. Data Collection: Acquire industry-specific data samples such as texts, emails, images, code, or audio that represent typical or desired interactions for the use case.
3. Data Cleaning and Augmentation: Eliminate bias, standardize format, correct grammar and token sequencing, and expand the datasets to include variations relevant to the domain.
4. Formatting data: Convert the data to the same format, encoding, and size as the base model was originally trained on.
5. Model Selection: Choose an industry-specific foundation model pre-trained with data that covers key vocabulary, speech patterns, regulatory requirements, or visual examples. Match the model size and type with the use case’s requirements.
6. Hyperparameter Tuning: Adjust parameters to optimize performance, prevent overfitting, and reduce costs. The most important parameters to adjust are batch size (data samples processed at once), learning rate (the speed at which a model learns), number of epochs (iterations through the dataset), weight decay (reduces overfitting, controls complexity), mixed precision (reduces GPU memory usage, increases processing speed), and number of warm-up steps (increases or decreases learning rate to stabilize model).
7. Validation: Evaluate the model’s response against industry-specific scenarios, user queries, long-form text output, and even visual content to measure its relevance, accuracy, and creativity. Measuring model output with a smaller sample of out-of-distribution data (OOS) provides additional information about its expected generalization beyond training set examples.

How to prepare training data for fine-tuning?

The process of preparing training data for AI fine-tuning involves multiple steps and considerations;

• Clearly define the objective: for fine-tuning and collecting high-quality, relevant data specific to the target task or domain.

• Ensure data is well-curated and organized: (i.e. remove duplicates, fix incomplete/outdated information, eliminate sensitive information, create clear input/output pairs)

• Format the data appropriately: based on the model type and framework.

• Text format: Many natural language data sets store text data in a simple text format such as .txt or .csv files.

• JSONL (JSON Lines) format: This is a simple format best for text applications such as chatbots, customer support, and content creation (each line is a valid JSON object) that is used by fine-tuning solutions like OpenLLM, OpenAI, Cohere, and Databricks. Example:

  {"inputs": "The restaurant was good, but the service was slow.", "targets": "neutral"} .

  Each line contains the input (e.g., a sentence or document), along with the expected output or target (e.g., sentiment label).

• CSV (Comma Separated Values) format: often used for tabular data or data that can be represented in a table format. The first row contains the column headers, and subsequent rows contain the data. Each cell in a row represents a value for a particular column.

• TFRecord format: used by Tensorflow, this binary format is used to efficiently store large machine learning data sets. Each record contains a serialized protobuf message, which can be read and written using TensorFlow’s built-in functions. This format can be used for ML tasks such as image segmentation or object detection, and can help to speed up data input/output operations.

• Image formats: Image datasets are typically stored in a format such as JPEG or PNG. Model frameworks such as Numpy and TensorFlow have methods to load and preprocess the images (e.g., resizing, normalizing, and data augmentation) before using them for training. Video formats: Video datasets are typically stored in a format such as MP4 or AVI. Similar to image data, they need to be loaded and preprocessed before use, followed by extracting individual frames, performing data augmentation, and creating input-output pairs for training.

• Audio formats: Audio datasets are typically stored in a format such as WAV or MP3 and need to be pre-processed, possibly by extracting features such as Mel-frequency cepstral coefficients (MFCCs), spectrograms, or chromagrams, before using them for training.

• Annotate or label the training data for AI fine-tuning: precisely, ensuring consistent and meaningful labels that align with the desired fine-tuning objective. Segregate the data into training, validation, and test sets, employing appropriate data splitting strategies to achieve a representative distribution.

• Balancing the data is crucial: to prevent bias so that large language model evaluation metrics are not compromised. Remove noise or irrelevant information from the training data to enhance the model’s learning process.

• Tokenization involves converting raw data: into numerical representations before it can be processed by neural networks. Tokenization might involve splitting text into words, converting images into pixel values, or representing audio as spectrograms.

• Data augmentation techniques involve artificially expanding the training dataset: by generating additional examples through transformations such as cropping, rotating, resizing, color adjustments, or noise injection – useful for domains where inconsistency or randomness is expected in the input. Use data quality checks and validation procedures to verify label accuracy and task relevance. Ensure that the data adheres to privacy and copyright regulations, removing any personally identifiable information (PII).

• Document the preprocessing, cleaning, and annotation: steps, maintaining transparency and reproducibility. Training data preparation for fine-tuning is an iterative process, so regularly evaluate and refine the data to address any issues or opportunities for improvement.

What tools are used to fine-tune generative models?

Hugging Face Ecosystem

• Transformers: Library for model fine-tuning across various architectures.
• PEFT: Parameter-Efficient Fine-Tuning library supporting LoRA, adapters, and prompt tuning.
• Accelerate: Simplifies training across different hardware setups (CPU, GPU, multi-node).

OpenAI Fine-Tuning

• Fine-Tuning Endpoint: Enables custom model training for GPT-3.5 using your own datasets.

Google Vertex AI

• Vertex AI: Supports model fine-tuning via tools like the Chat Completions API.
• Supports Hugging Face Models: Includes seamless fine-tuning of HF-hosted models within Google Cloud.

Frameworks for Fine-Tuning

• PyTorch Lightning: Modular framework for scalable model training and fine-tuning.
• TensorFlow: Widely used for ML training pipelines including generative model fine-tuning.
• MindSpore: Huawei’s framework offering an alternative to PyTorch/TensorFlow in specific ecosystems.

Natural Language-Based Tuning (NLPg Style)

The term “Natural Language Programming (NLPg)” isn’t an industry standard, but the concept aligns with prompt engineering and instruction tuning where models are guided using natural language instructions rather than code-level configuration.

Community-Driven Fine-Tuning & Enterprise Solutions

• Many tools from the community support GPT-3.5, LLaMA, and other open models using Hugging Face’s ecosystem.
• Enterprises often choose in-house fine-tuning platforms to ensure full control over sensitive data, privacy, and model behavior.

If you’re still wrapping your head around generative AI and how it works, our complete guide to Generative AI breaks down its core concepts, model types, and business applications.

Choosing the Right Model for Fine-Tuning

Choosing the correct generative AI model for fine-tuning is crucial to optimizing the benefits of customization for specific tasks or industry applications. Key criteria for deciding whether to purchase open source models like LLaMA, Mistral, or Falcon versus proprietary models like Claude or GPT-4o include looking at cost and security considerations, ease of customization, performance, and regulatory compliance.

The cost of using open source base models is cheaper since there is faster on-premises implementation without vendor lock-ins. This also provides better security. On the flip side, open source models are very hard to customize and might not realistically be feasible for startups or even some SMEs because this customization necessitates establishing specialized AI teams.

Open source models are generally lower performing because they have smaller pretraining datasets and less fine-tuning done by their creators. For example, Claude 3 users in late 2023 cumulatively claimed to have read over a trillion tokens. Mistral’s debut model in the same period was estimated to have a base of pretraining done on only around one trillion tokens total. Security is better with open source models when they do finally reach deployment stage, since fine-tuned models can run on premises instead of on a cloud.

Proprietary models are easier to learn and operate because they come with comprehensive onboarding training and continuous support. They have larger training and fine-tuning datasets provided by their vendors, which compensates for the absence of many hands-on hyperparameter tuning options available to open source developers.

Compliance with regulations is often easier with open-source models deployed on-premises, as they offer full control over data and infrastructure. However, proprietary models can also meet compliance needs when hosted by vendors that maintain certifications such as PCI DSS, SOX, ISO 27001, SOC 2, or HIPAA. The decision to fine-tune an open-source or proprietary model should be guided by business goals, task complexity, budget, customization needs, and regulatory requirements.

How to evaluate fine-tuning readiness of a model?

Evaluating the fine-tuning model readiness requires research into four areas: adapter support (LoRA etc), checkpoint availability (whether the right type of checkpoint is being offered), community tutorials, and API access/restriction.

1. Adapter support: Adapter support refers to whether the base model has additional layers such as Low-Rank Adaptation (LoRA), Adapters, or Prefix Tuning layers designed to allow fine-tuning a small number of parameters without modifying the rest of the model. It provides flexibility by enabling rapid experimentation with emerging PEFT methods, cost reduction by facilitating focused training of key layers, and collaboration by acting as adaptable ‘plug-and-play’ modules for knowledge sharing between models.

Evaluate adapter support by researching which adapters the model supports on its official website, Hugging Face, or other documentation sites. Typically, model maintainers provide documentation and/or usage examples indicating which adapters it supports as well (such as ChatGLM3’s README page on Hugging Face).

2. Checkpoint availability: Checkpoint availability refers to whether a model has ‘intermediate model states’ that capture the model parameters during training and can act as both a temporal ‘saves’ and as baselines for further training, evaluation, or deployment. They are essential during fine-tuning as they allow training progress to be saved and resumed in case of interruptions, and for capturing performance at various stages of training, which can be helpful for evaluating the impact of different hyperparameter settings.

Check if checkpoints are available by reviewing the model’s documentation (such as the LLama 3’s official website by Meta AI) or by contacting model maintainers or repositories (e.g., on Hugging Face and GitHub) which often state which types of model checkpoints (.bin, .ckpt, .pt, .onnx, .pkl) are available.

3. Community tutorials: Community tutorials e are important as models are updated frequently and PEFT fine-tuning technology is evolving rapidly. Review both text and video tutorials to ensure a model’s fine-tuning parameters are properly understood before beginning customization.

Assess the availability and quality of relevant community tutorials by searching code-sharing platforms like GitHub, video-sharing platforms like YouTube, and fine-tuning specific online communities for model plus fine-tuning related resources.

4. API access/restriction: Models with APIs can offer simple and accessible ways to customization and fine-tuning, but may place restrictions on which model parameters can be altered to ensure responsible customization and proper guard-rails. Some API-first co-pilot or assistant models currently have no means to fine-tune fine-tune a model due to regulatory (e.g., Hallucination risk) or technical reasons (e.g., heavy CPU/DPU infrastructure)

Evaluate API access and restrictions by reviewing the model’s documentation (including websites, white papers, etc.), performing practical implementation and testing, and reaching out for information to the model’s developers and maintainers.

Cost of Fine-Tuning a Generative AI Model

AI fine-tuning costs vary depending on GPU/computing power, dataset licensing, talent, number of iterations, and infrastructure weight. Large LLMs like GPT-3 and LLaMA-2 require extensive amounts of training data and customized training architectures. Their outputs are complex to control, making them more expensive to fine-tune.

The expansive nature of large language models also increases continuous costs in terms of technical infrastructure and data management, especially when iterating outputs and tracking performance metrics. Fine-tuning smaller models can simplify the process and lower engineering resource requirements.

One of the largest generative AI fine-tuning cost drivers is GPU. GPUs are crucial for generative models because they include multiple (hundreds or thousands of) cores that support massive parallel training of sophisticated neural networks. However, GPU hardware is expensive and there’s a global shortage due to burgeoning demand from the AI and blockchain mining industries.

Training a GPT-3-sized model (175 billion parameters) from scratch is a resource-intensive endeavor. Estimates suggest that the training process can cost between $500,000 and $4.6 million, depending on hardware, optimization techniques, and energy costs. OpenAI, for instance, utilized a supercomputer with over 285,000 CPU cores and 10,000 GPUs to train GPT-3. Such training runs typically involve GPU clusters ranging from 1,000 to over 10,000 GPUs, with training durations spanning several weeks to months. The associated costs for renting and operating these clusters can be substantial, potentially amounting to several million dollars, though exact figures vary based on numerous factors.

GPT-4o, launched by OpenAI in 2024, is a cutting-edge multimodal model capable of processing text, images, and audio. While OpenAI has not publicly disclosed the exact cost of training GPT-4o, industry estimates suggest that training this model exceeded $100 million. This cost includes massive computational infrastructure involving thousands of high-performance GPUs, substantial energy consumption, and extended training durations.

Fine-tuning BERT on a Tesla K80 GPU is cost-effective for small-scale tasks. With an approximate hourly rate of $0.296, the annual cost for continuous usage is around $2,592.96. Fine-tuning on datasets like MRPC can be completed in under 10 minutes on a single K80 GPU. Additionally, models like RoBERTa offer optimized performance, reducing training steps and further lowering costs.

Utilizing specialized datasets for AI fine-tuning enhances the accuracy, relevance, and compliance of model outputs. Licensing these datasets can cost from $10,000 annually for limited applications to over $1 million for extensive, industry-specific data. Engineering resources are influenced not only by model size and output complexity but also by the specialized expertise required, particularly in sectors like finance and healthcare.

Full fine-tuning of AI models is resource-intensive, necessitating substantial infrastructure for continuous retraining and optimization. Conversely, Low-Rank Adaptation (LoRA) offers a more efficient alternative, requiring fewer computational resources and enabling fine-tuning on local devices, thereby simplifying the process and reducing technical demands.

Post-deployment, AI models must undergo systematic evaluation across multiple metrics to ensure balanced, unbiased, and accurate outputs. This ongoing monitoring is crucial to detect issues such as data drift and model degradation, ensuring sustained performance and compliance with regulatory standards.

Use Cases that Require Fine-Tuning

Fine-tuning is used to help generative machine learning models perform better with accuracy, compliance, and user-friendliness. The fine-tuning use cases research, design, and implementation is a major focus of current LLM work. Fine-tuning is often used to assist generative AI tools in medical records retention, patient consultation, and pharmacy administration. In finance, it is used for risk management and planning services.

Fine-tuning is also used to help machine learning models perform better in creative, manufacturing, retail, and legal environments. In creative fields, it improves creative brainstorming, text-to-picture generation, and music composition. In manufacturing, it helps with decision-making and predictive maintenance. In retail, it is used to deliver customized customer service or providing engaging product information. In the legal domain, fine-tuning use cases include legal analysis, automatic contract review, and judicial research.

Fine-tuning generative AI models is essential across various industries, including virtual assistants and scientific research. Collaborations, such as that between Weights & Biases and Microsoft Azure, have streamlined the fine-tuning process, making it more accessible and efficient for enterprises of all sizes.

However, many companies, especially SMEs, face challenges in customizing these models due to a lack of structured and unstructured data. McKinsey emphasizes the importance of developing successful fine-tuned models to improve outputs, despite these data limitations.

Fine-tuning is essential for generative AI models to function across various industries. It provides users with carefully considered, user-friendly, and helpful generative AI tools. These tools benefit individual consumers, small businesses, multinational corporations, and the broader industry. Fine-tuning use cases have provided advancements in creativity, research, planning, and administration that rely on fine-tuned generative AI models.

Fine-Tuning for Compliance & Governance

Fine-tuning refers to adapting an existing general-purpose model by providing additional examples and training data which are domain or use-case specific. The general-purpose model has already been trained on huge generalized data sets so it contains base understanding of vocabulary, grammar, and general world knowledge. But it may not understand the nuance of a specific industry, or the strategic goals and compliance needs of an organization.

Fine-tuned AI models are in strong demand by organizations that want to maintain compliance and governance within high-regulation sectors such as healthcare or finance. Many AI models were developed using open source or public information which sometimes gives them biases or makes them unaware of the legal or regulatory rules that their outputs may violate.

Tuning AI for compliance should include adjusting the model’s content generation to avoid violating the data privacy standards of the Health Insurance Portability and Accountability Act (HIPAA) or the General Data Protection Regulation (GDPR). Fine-tuning can further ensure alignment with clear guidelines regarding copyright, advertising standards, and legal disclosures.

According to Reuters, the United States House of Representatives has several hundred bills that discuss some aspects of AI. AI Governance company Arthur reports in their 2024 State of Responsible AI Governance Report that despite this, approximately 97% of lawmakers still lack regulatory frameworks around AI governance.

A significant gap exists between policymakers and companies who run AI systems with respect to what it means to “build” AI systems. Models used by companies highlight the challenges of compliance auditing – as they face many challenges such as data privacy and data governance, system transparency, model evaluation, interpretation, and understanding of organizational context. The solution is to create specialized models with focused governance and compliance knowledge.

Such knowledge is not easy to impart to a Large Language Model. It is inherently complex and not standardized. A lot of it is embedded in legal and technical documents that are often not available on the public internet where the original foundational LLM would have obtained its knowledge from. These regulations also change frequently and are often layered across local, national, and international boundaries.

Fine-tuned models offer a critical solution to help organizations meet the operational and compliance norms where the foundational versions of the LLM may have lacked sufficient context. They can also improve the model’s auditability. Models adapted through AI compliance tuning for a company’s pre-agreed templates and formats make internal documentation and compliance procedures much simpler.

This helps to ensure that relevant stakeholders and regulators understand the internal workings of the LLM. They are more comfortable with the transparency, compliance, and governance processes for the model. Fine-tuned models are critical to meeting regulatory standards all around the world. Regulatory compliance is often one of the most critical determinants of whether the model is deemed appropriate for use.

Fine-tuning plays a vital role in ethical deployment, especially when mitigating bias or aligning outputs with policy standards. Learn more in our deep dive on Generative AI Ethics.

Tracking the Impact of Fine-Tuning

Measuring the impact of fine-tuning generative AI models allows teams to quantify the return on investment for development and deployment of customized AI solutions. Reduction in accuracy and hallucination distort model output quality, and a poor user experience reduces model adoption and engagement. Tracking the impact of fine-tuned models validates investments and guides further resource allocation.

Accurately measuring the impact of fine-tuning is important for strategic decision-making and fostering organizational understanding of the fine-tuning ROI trade-offs in deploying customized industry-specific generative models.

Get the input of employees and end users if possible for these core fine tuning performance metrics:

• Accuracy gain

  Fine-tuning large language models (LLMs) can lead to performance improvements in domain-specific tasks across various sectors. However, the extent of these gains varies depending on the specific application, dataset quality, and evaluation metrics used. While fine-tuning has shown benefits in areas like healthcare, legal, finance, and e-commerce, quantifying these improvements as fixed percentage gains (e.g., 71% in healthcare) is not substantiated by current research. Additionally, specific claims regarding F1 score improvements across domains such as banking, e-commerce, and software development lack accessible empirical evidence and therefore cannot be verified.

• Lower hallucination rate

  Recent studies have demonstrated that fine-tuning large language models (LLMs) can significantly reduce hallucination rates. For instance, the MAC-Tuning approach, which separates answer prediction and confidence estimation during fine-tuning, achieved up to a 25% improvement in precision by mitigating hallucinations in multi-problem scenarios. Additionally, fine-tuning LLMs with hallucination-focused preference datasets has led to an average 96% reduction in hallucination rates across various language pairs in machine translation tasks.

  Other factors influencing hallucination rates include the use of Retrieval-Augmented Generation (RAG) techniques, which ground model responses in external knowledge sources, thereby reducing the likelihood of generating non-factual content. Moreover, adjusting the model’s temperature settings during text generation can lead to more deterministic outputs, further minimizing hallucinations.

• User satisfaction

  User satisfaction is a critical qualitative metric for evaluating generative AI platforms. Recent studies have shown that users often find generative AI interfaces more user-friendly and intuitive than traditional search engines, leading to a preference for AI-generated outputs over conventional information sources. This shift places a significant responsibility on developers to fine-tune and customize AI models to ensure accuracy, relevance, and reliability, thereby maintaining user trust and engagement.

• Reduced prompt engineering time

  Fine-tuning can reduce prompt engineering time by developing the model’s downstream modality and context adaptation. By fine-tuning the model with high-quality data, the model can learn niche context, the specific information users need, and constraints or conditions that the output must meet. As a result, fine-tuning not only reduces prompt engineering templates but also the number of iterations. The ability to reduce these parameters is a useful measure of the model’s performance.

Some of these metrics (accuracy and hallucination rate reduction specifically) can be further improved using a post-hoc round of RLHF, but this should be done judiciously to avoid catastrophic errors or overfitting.

Industry Case Studies: How Fine-Tuning Transforms GenAI Outputs

Fine-tuning examples, in healthcare, legal, retail, and finance illustrate the benefits of using highly countable, objective metrics to measure progress before and after fine-tuning generative AI models. Researchers, corporations, and consultancies have released studies on a multitude of use cases across sectors showing improvements in performance through fine-tuning.

In healthcare, fine-tuned models have shown better performance and fewer hallucinations. A study on MedAlpaca, an open-source medical LLM fine-tuned on over 160,000 medical question-answer pairs, demonstrated improved accuracy and reduced hallucination rates compared to general-purpose models like GPT-3.5. Additionally, Harvard researchers found that fine-tuned LLMs based on PubMed literature and clinical case question datasets exhibited identical accuracy but significantly lower hallucination rates compared to unmodified LLMs.

In the legal sector, AI assists in reviewing, analyzing, and even generating contracts. ContractKen fine-tuned OpenAI’s GPT-4o Turbo on contract review and analysis data, enabling more efficient contract drafting and review. Similarly, McDermott Will & Emery utilized a fine-tuned AI model to analyze over 750 healthcare private equity deals, enhancing their ability to provide market analysis reports and improving the efficiency of drafting letters of intent.

Retail and e-commerce, sectors have benefited from fine-tuned generative AI models tailored to specific product categories and inventory trends, leading to increased conversion rates for product descriptions. PwC highlights that clients have experienced a 15–20% boost in request-for-proposal generation and a 25% reduction in call handling times for customer service teams by implementing fine-tuned generative AI solutions.

In finance, Deloitte’s deployment of Zora AI, a fine-tuned AI agent, resulted in a 25% cost reduction and a 40% increase in productivity within their finance team. Additionally, PwC reports that fine-tuned AI applications in financial services have led to a 15–20% increase in request-for-proposal generation, showcasing the revenue uplift potential of customized AI solutions.

Fine-Tuning Adoption Trends Across Sectors in 2025

Fine-tuning trends in 2025 are characterized by increased adoption, driven by the accessibility of generative models and fine-tuning tools from open-source providers. Models such as Meta’s LLaMA 3 and Mistral’s Mixtral offer immediate usability, cost-effectiveness, and enhanced privacy and control. While fine-tuning with minimal data samples is technically feasible, practical applications typically require more extensive datasets for optimal performance.

In the healthcare sector, a Q1 2024 McKinsey survey indicated that over 70% of healthcare organizations are either pursuing or have already implemented generative AI capabilities, reflecting a significant shift towards AI integration in healthcare.

Within the retail industry, an Accenture study found that 72% of retailers plan to use generative AI to fundamentally reinvent their operations.

While specific figures from BearingPoint’s September 2023 survey regarding fine-tuning as an “essential requirement” by 58% of organizations could not be directly verified, the trend towards prioritizing fine-tuning in AI strategies is evident across industries.

Enterprise initiatives by companies like OpenAI and SAP are actively exploring fine-tuning models for specific business applications, including customer service interactions and content creation. These efforts aim to enhance efficiency and tailor AI solutions to unique organizational needs.

Overall, the trends in 2025 indicate a robust adoption of fine-tuning practices across sectors, facilitated by the maturation of open-source models, heightened privacy requirements, and the standardization of generative AI model governance.

Before jumping into training workflows, it’s essential to evaluate whether your chosen platform supports token control, training input formats, and data security. Our AI Platform Evaluation Guide helps you make an informed decision.

Use Cases for Fine-Tuning in eCommerce and Retail

In e-commerce and retail, generative AI models offer substantial benefits in areas such as personalization, customer support, product catalog content generation, inventory summaries, and more. These applications help e-commerce and retail businesses enhance the experience both for consumers and internal users, driving revenue and reducing costs. In the marketing domain, generative AI is used for content creation and SEO optimization, and reliable methods of retraining marketing-specific ecommerce AI models to avoid brand voice and content duplication are possible.

Personalization: A highly competitive e-commerce and retail sector requires brands to stand out by identifying, targeting, and building relationships with loyal customers. Generative AI tools can help marketers fine-tune personalized messaging, targeting, and segmentation informed by customer behavioral analysis. It can also segment customers, profile key attributes of each segment, and recommend personalized messaging, marketing communication channels, and products to consume.

Customer support: Generative AI is used to build next-generation customer service chatbots that can engage with both e-commerce and retail customers. Recommending products, answering questions continuously, and being able to generate empathetic responses all increase efficiency versus traditional marketing. Additionally, chatbots that respond to customer inquiries and provide quick solutions to common problems based on fine-tuning data gathered from previous customer support interactions improve customer satisfaction.

Catalog generation: Cataloging products, variants, SKUs, and detailed specifications is a tedious, time-consuming, and error-prone process for most e-commerce and retail businesses, especially those with vast catalogs. Generative AI can automate the bulk of this work and output high-quality content at scale, saving time and money by ensuring data is updated consistently. Since generative models need to be able to handle brand-specific and variant-specific data unique to the brand that often lacks large media libraries, fine-tuning is crucial.

Inventory summaries: Generative AI models automate inventory management by collecting and analyzing information from various sources, identifying trends, and producing digestible reports for greater sales and margin trends. This helps reduce the risk of stockouts and anticipate needs arising from wrist control, avoiding overstock situations. The fine-tuned generative models can take in larger swathes of unstructured inventory data and distill it into more actionable guidance for improving working capital efficiency.

Fine-Tuning GenAI for Healthcare Accuracy and Compliance

Fine-tuning models for healthcare ensures safety, accuracy, and compliance in several ways. Safety is prioritized through carefully scrutinized fine-tuning data with strong manual safeguards. Fine-tuning leverages industry-specific knowledge for application in day-to-day medical services, avoiding problematic and inaccurate responses. AI models like LLaMA Fine-Tuned Healthcare Model can comprehend clinical language more accurately and efficiently after fine-tuning.

“Healthcare compliance” refers to efforts made by medical organizations to create policies, procedures, and safeguards to prevent and detect violations of laws, regulations, and administrative rules. HIPAA is the principal regulatory framework ensuring healthcare compliance in the United States. According to HIPAA Journal, “45% of the data breaches” are related to the healthcare sector.

Fine-tuning ensures accuracy in healthcare when models are adapted and adjusted with proper sources. The output of an AI model is highly sensitive to the training data. It is imperative that the training datasets are validated and verified. Due to the necessity of patient privacy and data security, privacy-preserving techniques must be incorporated into the AI model regarding the data they are trained on.

Healthcare AI fine-tuning has an immediate benefit in reducing hallucinations as well. A 2022 study from Harvard Medical School collaborators found an average hallucination rate of 6.5% across seven major healthcare LLMs. The rate of hallucinations present in the model is directly influenced by the training data.

When dealing with questions of healthcare compliance, ongoing human oversight and regulation are a difficult but necessary process. While compliance regulations are varied, ranging from territorial, geographic, profession-specific, and relative to the type of service provided, local governments and regulatory bodies have the responsibility to take preventative and ongoing steps to ensure patient security. This can take shape in the form of regular audits, data minimizing techniques, and regulatory compliance policies.

Legal & Financial AI: Domain-Specific Fine-Tuning

Legal and financial firms use GenAI to automate and improve accuracy in their high-volume but low-risk communications and documentation. Producing low-risk legal or financial documents like audit memos, compliance filings, risk summaries, purchase and sale agreements, alerts, technical guidance documents, and client reports are among the legal & financial AI use cases where fine-tuned models provide more tailored output than prompt engineering or manual processes.

One prominent area involves generating compliance filings where models have been fine-tuned on regulatory frameworks such as those from the US Securities and Exchange Commission (SEC) or the Financial Conduct Authority (FCA). By learning these frameworks, fine-tuned models produce relevant, jurisdictional-specific documents such as annual 10-K and quarterly 10-Qs and ensure that compliance filings meet all legal requirements while reducing the chance of violations.

In an example related to generating legal documents, in July 2023, Harvey.ai fine-tuned GPT-4 to produce short, first-draft legal contracts. A team at Harvard Law School researched Harvey.ai’s use cases and found their GPT-4 based gunprompts did not outperform six corporate lawyers on realistic contract drafting for a representative technology deal. However, more broadly in the legal industry, Lawgeex used over 30,000 NDAs and over 50,000 commercial contracts to finetune GPT-4. They released a beta of their offering which is under review in the legal tech community but was benchmarked to outperform Google Model Garden and Mistral models.

Fine-tuned legal and financial AI models can streamline the evaluation of a wide range of fundamental data. In annual reports, for example, these models can pull in various data and draft summaries of risk profiles, or review compliance documentation and highlight pending risk documentation. Generating and reviewing audit memos and legal compliance filings are key parts of an auditor’s daily work. By fine-tuning generative AI models on audit-related documentation, auditing firms can use these models to conduct document reviews and evaluations and provide relevant feedback to auditors.

OpenAI, Anthropic, Google, and Microsoft in addition to specialists like Harvey and Lawgeex are now working on producing improved, fine-tuned legal document solutions. The products are new and results have been insufficient to be reliable, but more competition will likely mean great improvements when performance data is shared in the medium term.

What to Expect from Fine-Tuned Models in 2025?

A fine-tuned model forecast for 2025 suggests the technology will offer enhanced accuracy and personalization in a wider range of industry-specific use cases. AI developers surveyed by Nobious said they expect ”an increasing emphasis on the fine-tuning of LLMs in the near future.”

Real-time fine-tuning and instant learning systems with near-zero latency will keep models continually updated for near-immediate response to changing industry or company conditions. Plugins will allow continuous training on real-world workflows with more current information.

Multimodal learning will become the standard, with fine-tuned models seamlessly combining data from different media to generate highly customized answers. Innovations in low-resource fine-tuning will empower startups and small businesses to fine-tune their own solutions within most sectors. Public sector institutions will accelerate the adoption of fine-tuned models for providing government services. Models will continue getting even larger, covering an even broader spectrum of domains. This broadening of domains might even include synthetic biology research and personalized medicine, as companies like Nvidia and Insilico Medicine are exploring.

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