Large Language Models (LLMs) can be very powerful right out of the box, but to truly unlock their potential, integrating with your proprietary data is key. In this article, we’ll explore the benefits of data integration with an LLM and how Stride can assist in maximizing the productivity and creativity of your team.
Artificial Intelligence (AI) has become an indispensable tool for businesses worldwide, and Large Language Models (LLMs) like ChatGPT have gained immense popularity. However, relying solely on generic LLMs limits the potential for productivity and creativity within your team. The solution lies in building your own LLM, powered by your proprietary data. In this article, we will explore the benefits of building a custom LLM and how Stride can assist you in harnessing its full potential.
Empowering Your Business with a Custom LLM
While ChatGPT is widely loved, it lacks knowledge about your business unless you provide it with context each time you use it. By building your own LLM, you can leverage the power of your proprietary data and create an assistant that understands your unique needs, speaks with your company's voice, and enhances productivity and creativity within your team.
The Importance of Data Integration
To get the best results from an LLM, whether using a public API/service or hosting your own, integrating your data is crucial. Stride specializes in LLM data integration and can guide you in seamlessly integrating your proprietary data stores. This integration enables the LLM to leverage your data assets behind the scenes, answering questions without explicit context, and turbocharging your team's creativity and productivity.
Determining the Optimal Data Integration Method
Stride's experts can help you determine the best approach for data integration based on your specific requirements. Two common methods include:
- Providing Context to a Pre-built Model: Sharing your proprietary data with a pre-built model allows it to gain contextual understanding and provide accurate responses. Stride can help you effectively share the necessary information for optimal performance.
- Fine-tuning a Model on Your Data: For some use cases, providing your data as context may present performance, accuracy or data security concerns. Stride can assist in fine-tuning a pre-built model using your proprietary data to achieve customized and accurate results.
The Advantages of Building Your Own LLM
Building your own LLM offers several benefits, including:
- IT Coherence: Stick to your preferred stacks and tools, ensuring compatibility with your existing IT environment.
- Independence from Cloud Providers: Gain independence from cloud-based AI/LLM providers, allowing you to have full control over your AI assets.
- Data Security: Ensure the confidentiality and security of your proprietary data.
- Predictable Costs: Have a clear understanding of the costs associated with your custom LLM, providing fiscal predictability.
The Process and Approach
Stride follows a systematic approach to building your custom LLM:
- Assessing your IT environment and addressing security and legal concerns.
- Discovering use cases for a custom LLM-powered application tailored to your business needs.
- Determining the best-fit stack, model, and data architecture for your specific requirements.
- Concurrently, iteratively building the application, integrating data, and training or fine-tuning the model.
- Piloting the application with key users, incorporating feedback, and rolling it out to train your team or organization.
Conclusion
Building your own LLM, powered by your proprietary data, can be a game-changer for your business. Stride's expertise in custom software development and data integration enables you to unlock the full potential of AI. By owning and leveraging a custom LLM, you empower your team, enhance productivity and creativity, and position your company for AI fluency. Take the next step and contact Stride today to embark on your journey towards building a powerful AI asset that aligns with your unique business needs and goals.
Stride Staff
