How to Train Generative AI Using Your Company’s Data
Learn how to train AI with your own data. This guide covers essential steps for data preparation and tailoring GenAI models to meet your business needs.
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We previously covered how to train an LLM using custom data, including the basics of tailoring and model evaluation. However, in this article, we’re shifting our focus to explain how to train generative AI using your company’s data.
We'll dive into the specific steps you need to follow, highlight additional considerations, and point out key areas to be cautious of. This guide will show you how to tailor your AI models effectively to meet your company's unique needs and objectives.
But first, let’s answer some essential questions you might have about data.
Key Takeaways
Effectively training generative AI using your company's data involves thorough data preparation, including cleaning, labeling, and validating datasets.
Regularly monitor and fine-tune the AI model to maintain accuracy, address bias, and adapt to new data.
Ensuring data privacy and compliance is critical when using proprietary data for AI training, especially in regulated industries
Can AI Compromise Your Data?
One of the most common questions business leaders in highly regulated industries ask is how to train AI models with private data without the risk of it being compromised.
Sharing your data with a third-party AI host can lead to security vulnerabilities, though this isn't always the case. The key factor is whether the AI is hosted on your own or vendor-owned infrastructure. So you have two methods:
Hosting AI on your infrastructure gives you full control over data security and compliance, which is crucial when training AI models with private data.
Hosting AI on a vendor's infrastructure could increase risks related to data privacy and third-party access.
At Multimodal, we primarily deploy AI solutions on client-owned on-prem infrastructure or Virtual Private Clouds (VPCs), ensuring maximum security, compliance, and control.
Ankur Patel, our CEO and founder, often emphasizes that the most secure approach is to build AI solutions on your infrastructure, ensuring that your data never leaves your control. This is especially important for companies in regulated industries like finance or healthcare.
However, some clients prefer us to host their solutions. This choice offers benefits like:
lower upfront costs,
reduced maintenance,
reduced deployment time
decreased IT overhead and resource allocation.
Ultimately, the decision on how to host your AI depends on your priorities and resources. While self-hosting is generally safer, vendor hosting can be more convenient and cost-effective—especially for companies with fewer regulatory concerns.
Once you have made a hosting decision that aligns with your business goals, it is time to prepare your data.
Preparing Your Company’s Data for Generative AI
Training generative AI using your company’s data requires a meticulous approach to ensure accuracy, security, and relevance. One of those steps is data preparation, which is a lengthy but crucial process.
Data preparation involves correcting errors, removing duplicates or irrelevant information, splitting the dataset into training and validation sets, and ensuring that the data is clean, clear, and properly formatted.
Three steps we take to ensure smooth data preparation:
Data cleaning
Data labeling
Data validation
Ensure smooth data preparation with these three steps
However, before taking these steps, there are two other factors to consider before training the AI model: data quantity and quality.
How Much Data Do You Need to Train AI?
The amount of data required to train an AI model varies depending on the complexity of the model and the specific use case. Typically, the more company data you have, the better the AI can learn patterns and nuances.
For example, training a generative AI to handle customer service inquiries might require thousands of conversations, while a model for internal process automation might need less data.
Check our successful customer story with Direct Mortgage, where we processed over 200 types of documents with our AI Agents, achieving a 20x faster application approval process and reducing costs by 80% per processed document.
However, more data of lesser quality isn’t always better.
How Important Is the Quality of Your Data?
Our founder always tells clients that it’s more effective to work with cleaner, more reliable data rather than noisy and incomplete data. By focusing on high-quality data, you can make better and quicker decisions.
Andrew McKishnie, our Senior NLP Engineer, points out that low-quality data is a frequent challenge in projects. To address this, we collaborate closely with clients right from the beginning.
We provide clear guidance on the data type needed and quickly assess its quality.
We also help companies prepare their data. If it's insufficient, low-quality, or doesn’t meet our standards, we work closely with them to improve it or find additional data. This collaborative approach ensures we build robust and effective AI models using high-quality data.
How To Train AI on Your Own Data
Obviously, the first step will be establishing a clear objective of what your AI model needs to do.
Then, you must gather a significant amount of representative company data from across your organization. That includes existing data, transaction logs, customer feedback, and any other relevant datasets. This ensures your own AI model can generalize well to new, unseen data.
If your company’s data is limited, consider combining it with external datasets to enhance training.
Begin by identifying and collecting data from various sources within your company. This includes CRM systems, sales databases, internal communications, and any other relevant information repositories, depending on the AI use case you wish to develop.
The goal is to create a comprehensive dataset that reflects the diversity and complexity of your business operations.
For instance, in loan underwriting, you might collect data from credit reports, income statements, employment records, and previous loan applications and performance. Similarly, for insurance, relevant data could include claim histories, policy details, risk assessments, and customer demographics.
Ensure that structured data (like SQL databases) and unstructured data (like emails or documents) are properly integrated and formatted for AI model training.
Step #2: Data Cleaning and Preprocessing
Once you’ve collected your company data, the next step is to clean and preprocess it. This is crucial because dirty or inconsistent data can lead to the development of inaccurate AI models.
Start by:
removing duplicates,
correcting errors,
filling in or discarding missing values,
standardizing formats across datasets to ensure consistency.
Data cleaining and preprocessing are one of the most important steps for successful AI implementation
If your data includes customer information from various sources, ensure that names, addresses, and other details follow the same format. When possible, we highly suggest developing data-cleaning scripts in a scripting language like Python to automate this process.
Preprocessing also involves transforming the data into a format suitable for training. This might include tokenizing text data, normalizing numerical values, and encoding categorical variables.
Step #3: Ensuring Data Privacy and Compliance
When training AI with your company's proprietary data, ensuring compliance with data privacy regulations such as GDPR or CCPA is essential. This involves anonymizing sensitive information, securing datasets during transfer and storage, and maintaining transparency about how the data will be used.
To mitigate these risks, deploy AI models on your own infrastructure or in a Virtual Private Cloud (VPC), as we suggested earlier. That way, you retain full control over data security.
Step #4: Fine-Tuning and Customizing the Model with Company Data
Fine-tuning involves adapting pre-trained large language models to your specific needs using your company’s data. This process leverages the existing knowledge embedded in the models and enhances them with domain-specific insights from your data.
To begin the fine-tuning process, divide your data into:
Training data set: to adjust the model’s parameters.
Validation data set: to tune hyperparameters.
Test data sets: to evaluate performance.
To effectively customize an LLM using your company’s data, you should also:
Enhance Precision: Tailor the model to grasp industry-specific jargon and terminology, ensuring more accurate and relevant outputs.
Develop Expertise: Train AI on domain-specific data to handle queries with expertise beyond generic models. Customizing your LLM with company-specific data sharpens brand alignment, improves insights, and supports strategic decision-making.
Boost Personalization: Use customer data to refine the model’s ability to predict and meet customer needs.
Effectively customize an LLM using your company’s data
Step #5: Evaluating Model Performance
After fine-tuning, it's crucial to evaluate your model's performance rigorously. Monitor the model for bias and hallucinations, or other unexpected behaviors, especially when dealing with sensitive company data.
Regularly update and retrain the model as new data becomes available to ensure it remains accurate and relevant.
Let’s say you want to train a generative AI tool to automate contract cancelability determination. You must evaluate its performance by comparing its decisions to those made manually.
In our work with a loan origination company, we achieved 100% automation of this process and reduced the average processing time per contract from several hours to just 3 minutes.
That’s why it’s essential to continuously monitor and adjust the model to maintain accuracy and efficiency, ensuring it consistently delivers reliable results as new data and contract variations emerge.
You should also decide which AI KPIs you’ll use to assess performance, business impact, and ROI.
Step #6: Deploying the Trained Model
Once the AI model meets performance expectations, it’s time to deploy it into your company’s workflows. Ensure the deployment environment is robust and scalable, capable of handling the model’s computational requirements.
Integrate the model with your existing systems, whether a CRM for customer interactions, an ERP for business operations, or a custom application for specific tasks. Monitor the model post-deployment to ensure it functions as expected and provides value.
Need Help? We’re Here for You
We specialize in helping companies prepare, integrate, and train AI models using proprietary data. Whether starting from scratch or looking to enhance your existing AI capabilities, our team offers tailored solutions to meet your needs.
If your data is limited, we can also help you source additional datasets to ensure your model is well-trained and effective. Schedule a free 30-minute call with our experts to discuss how we can support your AI initiatives.
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