Crafting Competitive Edge by Training LLMs with In-House Data
Sravanthi Pulijala
August 24, 2023

In today's data-driven landscape, companies possess a goldmine of untapped potential within their own datasets. This blog unveils the transformative potential of utilizing an organization's proprietary data to train large language models. By harnessing this internal wealth of information, businesses can develop tailored AI models that grasp industry-specific nuances and contexts.

By understanding and utilizing the capabilities of large language models, businesses can tap into unprecedented opportunities for progress, innovation, and success in the digital age.

Why training on organization’s data is important:

Companies have access to in-house data that is specific to their industry, customers, products, and services. This provides opportunities for developing context-aware and more accurate language models. In-house data also allows companies to protect their proprietary information.

Also, it is important because it enables the model to learn the specific language, terminology, and context used within the organization, resulting in enhanced natural language understanding and communication capabilities tailored to the organization's unique needs and domain.

Essential steps for organizations to Begin with Large Language Models:

1. Find the Right Fit:

Start by identifying where LLMs can make a difference for your unique challenges. Whether it's improving data analysis, enhancing customer support, or refining content creation, pinpoint areas where Generative AI can be used. This step involves understanding your business goals, pain points and growth opportunities.

2. Quality Data Matters:

Strong LLM performance relies on high-quality training data. Ensure that the data you use for training and fine-tuning is both reliable and reflective of the problem you're tackling.

3. Team Up with Experts:

Working with LLMs requires AI and data science expertise. Collaborating with AI specialists and data scientists offers valuable guidance throughout the process. They can help with model selection, fine-tuning, and integration into your existing systems.

4. Tailor for Precision:

While LLMs start with general training, customizing them with your own data can boost their performance. Fine-tuning with your specific datasets helps models adapt to your industry's language and context.

5. Regular Evaluation:

Regularly assess how well your LLMs are performing and monitor their results. Use metrics and feedback loops to measure accuracy, effectiveness, and impact. Continuous improvement based on monitoring insights keeps the models effective and relevant.

Comparison between custom training of large language models and the utilization of pre-trained models:

Custom Training of Large Language Models Using Pre-Trained Models
Purpose Purpose
Tailored to specific domain or knowledge. Rapid prototyping of new applications.
Fine-tuned for specific tasks, e.g., chatbots. Experimentation with diverse tasks and ideas.
Enhanced privacy by training on sensitive data. Access to AI capabilities without complex infrastructure.
Advantages Advantages
Precise solutions for unique requirements. Quick design iterations and feedback gathering.
Highly accurate results for specific tasks. Low investment in terms of resources and expertise.
Ensures data privacy for sensitive information. Accessible to organizations of all sizes.

Steps involved in training large language models on custom data:

1. Preparation of Custom Data:

Data Collection: Gather a diverse and representative dataset that aligns with the specific domain or task you intend to train the model for. This dataset should encompass relevant text sources, such as articles, documents, websites, or user-generated content.

Data Cleaning: Clean the collected data by removing irrelevant, duplicate, or noisy content. This ensures that the model focuses on high-quality information.

Data Formatting: Structure the data in a consistent format suitable for the chosen large language model.

Data Augmentation: To enhance model robustness, consider augmenting your dataset with variations of existing data. Techniques like paraphrasing or introducing synthetic data can help the model generalize better.

2. Selection of Appropriate Language Model:

Organizations have two primary options: open-source models and proprietary models. Each has its own set of advantages and considerations.

Open source LLMs are models whose code and architecture are publicly available.

Proprietary LLMs are developed and owned by specific companies or organizations.

Aspect Open Source LLMs Proprietary LLMs
Ownership and Access Often developed by the community; accessible to all Owned by specific comapanies; access might require arrangements/licenses
Cost Generally free to use Typically involves licensing fees or subscription cost
Customization Can be customized and fine-tuned according to needs Customization might be limited by providers policies
Model Control More control over the model's behaviour and parameters. Limited control over inner working and fine-tuning
Use Cases Use Cases Flexible for a wide range of applications Might have restrictions on certain use cases

Companies can select any of the options mentioned below to select a model.

Option 1: Crafting a custom model

This hands-on approach involves creating a customized Large Language Model (LLM) from the ground up, offering unparalleled control over training data, privacy, and security; however, it requires substantial data, computational power, and specialized AI expertise, with associated costs potentially reaching multimillion dollars for training a model like OpenAI's GPT-3, necessitating a careful assessment of benefits versus resource investment.

Option 2: Ready-made Solutions

Selecting a pre-built solution, such as integrating an AI-driven code completion tool into existing software development, offers a convenient and efficient path; yet, customization might be limited, and these tools may not grasp specific coding styles, warranting consideration.

Option 3: Adapting Existing LLMs

A balanced choice involves adapting a pre-trained LLM to match specific needs; this quicker and cost-effective approach contrasts with building anew, with the decision between proprietary and open-source models hinging on business needs, resources, and potential risks.

If you're interested in exploring pre-existing LLMs, our previous blog post: Exploring the World of LLM Models can provide valuable insights.

Option Pros Cons Cost Flexibility Which kind of companies should select this option?
Crafting a custom model - Control over training data, privacy, and security
- Tailor models to exact needs
- Avoid overreliance on a few AI providers.
- Demands a substantial amount of data
- Requires specialized expertise
- Risk of bias
High Companies that have unique operations or non-conventional products choose proprietary software.
Ready-made solutions - Rapid implementation
- Quick and easy to setup
- Versatile
- Limited customization
- Limited Accuracy
- Might lack familiarity with your specific coding style.
Low Companies that want to save time and money and have less complex operations choose ready-made solutions.
Adapting existing LLMs -Quicker and more cost-effective than building from scratch
- Can be finetuned
- Requires lots of domain expert skills to train, fine-tune, and host an open-sourced LLM. Medium Companies that want to save time and money but still have some level of customization choose adapting existing LLMs.

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3. Fine-tuning of the Model:

Fine-tuning an LLM involves adjusting a pre-trained language model using task-specific data to make it perform well on a specific task or domain. 

  • This can be done by freezing some layers and adjusting the parameters of the remaining layers. 
  • Techniques such as hyperparameter tuning, transfer learning, and regularization can be used to improve the model's performance. 
  • These techniques can enhance accuracy, efficiency, and robustness, resulting in improved overall model quality. 

4. Evaluating the Performance of the Model:

Model evaluation is essential for understanding the effectiveness and usability of a language model. Standard metrics such as classification accuracy, perplexity and F1 score can be used to evaluate the performance of your custom model.

Metric Description
Classification Accuracy Percentage of correctly classified samples
Perplexity Measurement of how well a
probability model predicts a sample.
Lower values indicate better predictions.
F1 Score Weighted average of precision and
recall. It measures the accuracy of a
model in identifying the positive

5. Deployment:

Deployment of LLM refers to the process of making a large language model accessible and operational within various applications, platforms, or systems. 

Once the model is trained, it can be deployed via an API endpoint. This allows other applications to make use of the model's predictions and feedback results in real time.

Steps for LLM deployment:


Companies can leverage their in-house data to train more accurate language models. Insourcing allows companies to overcome challenges associated with acquiring data and protect their proprietary information.

We hope you found our blog post informative. If you have any project inquiries or would like to discuss your data and analytics needs, please don't hesitate to contact us at info@predera.com. We're here to help! Thank you for reading.
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