Custom AI Models vs ChatGPT: A Guide to Private Large Language Models

What can custom AI models do that ChatGPT can't? Which open-source model is best for your business? 

Discover how to build enterprise-grade AI solutions using Mistral and Llama models with step-by-step guidance from a leading AI developer.

About Yash Gad and Ringer Sciences

Yash Gad's journey into AI began with an unconventional background in biophysics and computational neurosciences. His doctoral research focused on developing neural network models, establishing a foundation in traditional AI frameworks. In 2013, Gad transitioned from academic research to the agency world, where AI was discussed primarily in terms of machine learning and Natural Language Processing (NLP) tools.

Working alongside analysts, Gad discovered opportunities to apply his research-based models to marketing challenges, particularly in processing and understanding behaviors and network effects. This led to the founding of Ringer Sciences. The company name reflects its role – bringing in expert “ringers” whose expertise might initially face resistance but ultimately proves invaluable.

The launch of ChatGPT marked a significant shift in demand for AI expertise. While Ringer Sciences had been implementing AI solutions for years, many technically proficient companies suddenly struggled with practical AI implementation. Even organizations with sophisticated IT and development teams faced significant barriers when integrating AI tools into their workflows.

In response, Ringer Sciences developed a specialized focus on custom language models. This approach moves beyond simple ChatGPT integration to address organizations' needs for handling sensitive or highly specialized data. The company helps clients develop secure, customized AI models that operate within their existing IT infrastructure while maintaining strict data privacy standards.

3 Strategic Advantages of Custom AI Model Training for Businesses of All Sizes

Custom AI models benefit businesses, from small operations to large corporations. The advantages center on three key areas:

Protecting Proprietary Knowledge: Data security and intellectual property protection represent crucial concerns. Many businesses possess valuable proprietary domain knowledge that forms their competitive advantage.

When using public tools like Claude and ChatGPT, there's an important distinction between “protected” and truly secure data. While these services may offer security from external access, they have complete visibility into all data sent through their APIs.

Without proper monitoring and structural safeguards, your sensitive information sent through API calls is “out there,” and terms of service for AI tools, including platforms like Jasper AI, place the responsibility for data protection on the users. While many users trust AI providers not to train their models on sensitive data shared through APIs, there's no absolute certainty about how your data is used. 

All of these factors make custom AI models more valuable.

Specialized Task Optimization: Custom models can be precisely calibrated for tasks crucial to unique business operations. This focused approach results in higher performance and accuracy than generic AI solutions.

Gathering Valuable Prompt Analytics: Prompt analytics provides unprecedented insights into how users interact with AI systems. By analyzing how customers formulate prompts and engage with AI chatbots, businesses can gather valuable data about customer behavior, needs, and thought processes, creating new forms of intellectual property.

Custom AI Model Case Study: AI-Powered Product Launch Branding

Ringer Sciences' first custom language model implementation focused on a product launch branding exercise. The client had numerous assets, including past press releases, branding documents, and competitor product launch information. They initially wondered if ChatGPT could help synthesize this data for branding purposes.

The team began by collecting and organizing the data and scraping internet sources for additional relevant information. They tagged all content with specific information about target audiences and branding focus areas, creating an organized framework for the eventual model.

For implementation, they used a localized Llama model with a RAG approach, allowing it to interact with the tagged data based on specific queries. Rather than fine-tuning the base model, they structured it to reference the organized data as needed.

They created a lightweight chatbot interface and tested it with branding-related prompts, such as generating mission and vision statements with specific focuses like patient-centric or healthcare provider (HCP) orientations. While it took some time to optimize the prompts correctly, once refined, the model could quickly generate multiple drafts.

The entire process – from data gathering to model setup to generating a presentation deck for the C-suite – took just two weeks, dramatically faster than traditional agency timelines. When presented to the C-suite, the executives' main feedback involved combining preferred sentences from different drafts into final versions. The output received strong approval overall, mainly because it was thoroughly grounded in the company's existing data.

Custom AI Model Case Study: Standardizing Social Media Reports

Agencies frequently send social media reports, earned media reports, and conference event performance reports.

Ringer's client was struggling to make sense of all these different reports. They approached Ringer Sciences to build an AI model to interpret these various reports according to their understanding of the metrics. The challenge was complex: ten different agencies might use ten different terms for the same metric, and some metrics need to be normalized due to different scaling factors. Additional considerations included different metrics for various platforms like LinkedIn versus X and variations based on different areas.

Using a RAG approach, the team built an AI framework incorporating the client's understanding of how different metrics fit together. Instead of creating a chat interface, they developed a simple upload system where users could submit PowerPoint presentations from agencies and receive back a fully normalized Excel data sheet.

The solution proved remarkably effective, processing hundreds of previously received presentations that would have required extensive manual effort to analyze. More importantly, it provided a repeatable process—any new reports could simply be uploaded to the tool to generate normalized data.

This practical application demonstrated how custom AI could transform a time-consuming manual process into an efficient, automated system while ensuring consistency in data interpretation across multiple sources.

The 4 Components for Creating Custom AI Models

A custom AI model comprises four distinct components that work together. 

A Secure Data Repository: The first component is on-premise storage or storage within a cloud solution. The data stored here should be specifically structured and tagged to support particular use cases incorporating AI.

The AI Model: Options include localized solutions like Mistral or Llama 3 that operate independently of internet connectivity. You can fine-tune these models to learn from specific datasets or implement a RAG approach for information processing.

What is RAG? Retrieval Augmented Generation (RAG) is the key to accurate AI responses. 

RAG involves retrieving data from a specified source and augmenting the AI model's generation process with this information. This technology appears in popular AI platforms like ChatGPT and Claude. When users upload documents such as Excel sheets or PDFs, they are utilizing RAG functionality, creating a temporary data source the AI can reference while generating responses.

Many users mistakenly envision ChatGPT as having a comprehensive knowledge base stored in the cloud. However, while these models have extensive general knowledge, they lack access to specific business information unless explicitly provided through RAG. Without proper guidance, AI models may attempt to fill knowledge gaps by making assumptions, leading to “hallucinations” – instances where the AI generates plausible but incorrect information.

You don't need to mention RAG by name in your prompts specifically. Instead, you can simply instruct the model to “only pull data from these sources” or “do not make anything up,” with additional commands like “don't lie” or “don't extrapolate.”

The User Interface: Customizations might include buttons, reference materials, or other features designed to match your specific business needs. For example, not every custom AI model requires a chat interface. The interface should reflect your business processes.

The Analytics Layer: This layer captures and analyzes user interactions with the system and represents valuable intellectual property for the business.

Choosing a Secure Custom AI Model: Which Open Model is Right For You?

For organizations with strict security requirements, custom AI models offer several options:

• Walled-off models ensure data never leaves the organization's IT infrastructure
• Models that operate without internet connectivity
• Fully on-premise solutions for organizations with previous security breaches, hosting everything on local machines accessible only through internal networks

One Ringer client uses an innovative security solution that wraps its various AI models in an API wrapper. This wrapper acts as an intermediary layer that can flag sensitive data, block certain information from being transmitted, and control what data is sent to the AI model. It provides a single entry point to multiple models, allowing IT teams to manage which models are available while maintaining consistent access methods.

Pro Tip: Hugging Face provides a valuable leaderboard that tracks public, private, and open-source models and shows their various licensing arrangements.

Mistral vs. Llama

Two prominent open-source options for building custom AI models are Mistral and Llama. 

In side-by-side comparisons, Mistral has generally demonstrated superior performance across multiple metrics, including faster processing speeds and better performance with smaller models. 

Both models come in different sizes or tiers with varying weights that affect computer storage requirements, hardware specifications, and inference speed.

Mistral shows particular strength in processing unstructured or “messy” language, such as social media content. Llama 3 is a capable generalist model and benefits from extensive community support, particularly through resources like the Llama Lounge subreddit.

Open models like Mistral and Llama are rapidly closing the performance gap compared to ChatGPT and Claude. In some specialized domains, such as healthcare and technical areas, these open models can even outperform their proprietary counterparts, though ChatGPT maintains a significant lead in general applications.

Other Notes From This Episode

• Connect with Michael Stelzner @Stelzner on Facebook and @Mike_Stelzner on X.
• Watch this interview and other exclusive content from Social Media Examiner on YouTube.

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