The expanding and extending capabilities of OpenAI’s GPT (wiki) and other similar specialized Generative AI platforms have made companies explore untapped insights and business opportunities hidden within the data bed. As the abundance is available in the form of LLMs, Foundation Models, and Generative AI models, it’s imperative for businesses to optimize these models with their specific business data to scale and extract value.
Fine-tuning is a strategic technique that allows the transformation of open-source and generic pre-trained AI models into customized systems trained with specific business data to fulfill targeted business objectives. As different industries have different use cases for adopting Generative AI models, first it’s important to trace them to accurately address the Generative AI strategy within an organization.
In this blog post, we will be discussing different industry gen-AI use cases, their significance in business transformation, and steps the companies should adopt to make the generative AI model account for relevance and accuracy of information.
Industry Wise Generative AI Business Use Case: Examples of Fine-tuning
Although almost every industry has identified the importance of generative AI in day-to-day operations, there are a few that have moved the needle faster, i.e., Healthcare & Lifesciences, Banking, Finance and Insurance Services, Supply Chain & Logistics, Retail & Commerce, Media & Advertising.
Let’s check industry-wise generative AI business use cases to understand how these industries are taking advantage of their business data to generate and scale value from Generative AI models.
1. Healthcare & Lifesciences
Healthcare is the most data-intensive industry, generating more than 30% faster data than other industries across the world. Mobile-based services like telemedicine and telehealth, generating data, storing and retrieving data from EHR to become compliant with FHIR, healthcare, medical, and life sciences services have come a long way and are operating digitally.
The point at which they lack currently is providing personalized nutrition & treatment for overall well-being and early detection of diseases or their subtle signs and diagnostics, requiring extensive human efforts 24×7. The shortage of skilled and experienced healthcare providers in the industry is yet another reason the industry looked for machine-based solutions.
Here come Generative AI models with capabilities to generate new text, images, audio, and video. Fine-tuning the LLMs or foundation model with specific or specialized healthcare data enables organizations to build their generative AI models that help them deep dive into insights and the history of a patient in one go.
Besides personalized treatments, comprehensive and accurate patient health data can be leveraged to simulate complex surgeries and even enable surgeons to perform AR surgeries, improving patient outcomes.
2. Banking, Finance, and Insurance
Being another industry dealing with critical customer data, BFSI companies extensively spend on human efforts and resources to analyze customer transactions to personalize financial products and services.
Client’s experience with the BFSI directly affects their revenues, but these institutions have to deal with multiple reports and financial documents. Going with the manual ways of reviewing and approving documents delays decision-making and negatively impacts customers.
BFSI here can fine-tune generative AI models to empower and enhance customer and employee experience. All this is possible through tuning existing chatbots or voice bots with LLMs to support self-service.
Integrating self-service customer bots can help organizations address repetitive customer queries, whereas an AI agent bot enables service agents to quickly look up customer profiles and grab insight about their customer journey points to make customer relationship management more profound and personalized.
3. Supply Chain & Logistics
Good, clean data is at the core of successful AI use cases and supply chain management and logistics is one such industry that captures the data of every customer journey. Introducing Generative AI and tuning the model to understand whether they have good data, how the data is inputted, where it has started generating insights, and where the data sets need some further work helps companies get their data in line and enable them to enrich that or put it to different use.
Summarization is also another Generative AI use case with supply chain and logistics, where companies can make the most out of feeding customer or business data to the AI model. This way, organizations can get summaries of the customer history, inventory, and stocks for a particular quarter, a summary of business data and sales, revenue generated, and other different ways, which is not possible to do with existing applications.
4. Retail & Commerce
Retail and commerce is also a data-intensive business. Generative AI models can be used to automate workflow on the back office operations and help achieve 70% efficiency through eliminating repetitive tasks.
However, this level of efficiency in retail and commerce is not possible until the AI model is tuned to perform the specific back-end jobs. Right from data entry to generating reports, AI models can be tweaked to perform jobs like scanning documents for data extraction and converting them into rows and columns for better scanning through the human eye.
Data segregation into rows and columns is also significant, as data storage digital platforms store data in rows and columns to run queries on the data for insights generation. Personalized marketing campaigns, customer details analysis, and predictive analytics are some other industry-specific Gen AI use cases where fine-tuning the models is helpful.
5. Media & Advertising
Just like other industries, personalization is the core of media and advertising offerings. As the industry is into helping brands differentiate themselves from their competitors, media and advertising firms secure and deal with immense customer behavioral data such as names, age, gender, location, interest in something, habits, and others.
If leveraged appropriately, this customer data could bring significant insights into predictions and reach an appropriate group of audience for targeted messaging and advertising. Since the data is available within companies, technologies were not that advanced enough to make insights from it.
With generative AI models, it is possible to extract insights in the formats and structure a human brain wants. Right from developing recommendation engines to support core business systems to generating relevant advertising copies, images, audio, and videos, Generative AI models can do it all better and faster than humans.
AI hallucination and generic response can be a challenge in using the model, but tuning them based on the required outcome guidelines, and intent can help overcome the challenge.
Step-by-step Process to Fine-tune Generative AI Model
To fine-tune a generative AI model, you must have an AI model. As most organizations begin their Generative AI journey with existing models like GPT-3 or similar platforms, it is better to train this model with proprietary data rather than train a model from scratch.
Fine-tuning is one of the crucial steps of generative AI strategy that allows companies to make the existing model specialize in answering specific business queries. Fine-tuning a generative AI model is a several-step process. Companies first need to select a pre-trained model that suits the business use case. For tuning the model, the next step is to prepare the sample data that the model needs to be trained. The next step is to iterate the model to improve its performance.
Let’s discuss the step-by-step process for fine-tuning the Generative AI model:
1. Decide Whether You Want to Fine-tune the Model
Many organizations are well-tuned with off-the-shelf generative AI models, helping them generate the outcomes as new text or images or audio or video. Fine-tuning is suited for times when you have a small amount of data to train it with.
Fine-tuning is not helpful when you have a large amount of data. In such scenarios, you have to train the model from scratch to make it perform well. However, fine-tuning can still be required in case you want to improve the performance of your model further.
Fine-tuning can help train the model to perform a specific task that is different from the task the pre-trained model was originally trained on. Henceforth, it is important to decide whether you want to fine-tune the model or if it requires training from scratch.
2. Select a Pre-trained Model
Now, you should select a pre-trained model that is well-suited for your business use cases. Before the launch of DeepSeek, many organizations widely adopted OpenAI’s ChatGPT as the base AI model to begin with. As several other pre-trained models are available now and even doing human-like reasoning, you can select one whose knowledge you want to leverage. You can go with selecting categories such as:
- Multimodel.
- Computer vision.
- Natural language processing.
- Audio.
- Tabular.
- Reinforcement learning and others.
You can check the compatibility of the model with your environment and the tools you already use to run them. You should also check the status and license of the model, or you can also go with an open-source license if your requirements are not commercial. Ensure that you checked all clauses before fine-tuning the pre-trained model.
3. Prepare Your Sample Data
Preparing sample data means cleaning and preprocessing your data to make it suitable for AI model training. It includes data cleaning and preprocessing to remove inconsistencies, missing values, or irrelevant information.
You can further break the data into training and validation sets to validate the outcomes and performance of the model to prevent overfitting. Additionally, final checks and export of data are essential to ensure the dataset is appropriately formatted, properly split, and compatible with the model.
4. Iterate Your Model
Once you fine-tune the model, it’s important that you assess the model’s effectiveness using the validation set. The outcomes must be checked against accuracy, precision & recall, perplexity, or BLEU score, and F1 score.
If the model doesn’t perform as expected, a comprehensive analysis of failure points must be conducted. It will take you through identifying misclassified examples, response coherence, and checking for biases and inconsistencies.
If required, you can also modify model architecture to close the gaps like increasing hidden layers, updating attention mechanism, or changing token embeddings to improve the model performance.
Another way that can be considered to iterate on the model for model accuracy and performance is to increase the dataset size, improve the data quality, and address the data biases.
After making all adjustments, you can retrain the model, validate the results, and repeat the iteration process until the desired response is achieved. The iteration process is an important step that enables you to refine your model progressively and derive better accuracy, reliability, and domain-specific applicability.
Conclusion
Typically the process of fine-tuning a generative model is comprehensive and requires enough technical expertise and a solution-oriented mindset to integrate it into the existing IT system. Oftentimes, organizations fail to make the most from such initiatives if they rely on in-house resources who try to learn and implement the quickly accumulated knowledge on enterprise-level applications or use cases.
If you consider generative AI strategy as a core component of your decision-making and revenue-generation process, it’s always recommended to get the solution implemented by an experienced and technically sound solution provider.
