#6: CXO Conundrums: Build own LLM or leverage external LLMs(ChatGPT/BARD) or better still integrate both- external and internal?

What is a LLM?

We are all getting the hang of "LLMs" now. Simplistically speaking, a Large Language Model (LLM) is like a computer brain that understands and generates human-like text. In Generative AI, LLMs are trained on lots of text data to learn the patterns and meaning of language. They can then generate new text, complete sentences, answer questions, and have conversations with humans. LLMs are used in chatbots, language translation, content creation, and more, making human-computer interactions more natural. However, careful handling is needed to address ethical concerns and potential biases. LLMs are powerful tools that help computers understand and generate text like humans, shaping the future of technology.

Why should building own LLM even be an option for any organization?

In an organizational setting, a key question confronting technology leaders is whether to build their own LLM inhouse or leverage an external one like ChatGPT or BARD. There are several reasons which may make you look at building your own internal LLM:

1. Customization and Control: By building your own LLMs, organizations have greater control over the model's behavior, responses, and customization. They can fine-tune the model to align with their specific needs, brand voice, and user experience requirements. This level of customization allows organizations to tailor the LLM to their unique context and achieve better alignment with their business objectives.
2. Domain Expertise: Many organizations operate in specialized domains or industries with specific terminology and knowledge requirements. Building an internal LLM allows them to train the model on domain-specific data and fine-tune it to excel in their specific domain. This specialized knowledge enables the LLM to provide more accurate and contextually relevant responses, enhancing the user experience and the quality of generated content.
3. Data Privacy and Security: Some organizations handle sensitive or proprietary data that cannot be shared with external models due to data privacy concerns. By building their own LLM, these organizations can keep the data within their infrastructure, ensuring better control over security protocols, data access, and compliance with data protection regulations. This approach enhances data privacy and security.
4. Competitive Advantage: Developing an internal LLM can provide organizations with a competitive edge. It allows them to differentiate their products or services by leveraging unique language capabilities that are tailored to their specific market or customer needs. The ability to offer more accurate, domain-specific, and customized responses can enhance user satisfaction and help organizations stand out in the market.
5. Intellectual Property Protection: Building an internal LLM allows organizations to retain intellectual property rights over the model and its underlying technology. This can be a significant advantage, especially if the organization sees its language model development as a valuable asset for future innovations or potential licensing opportunities. Intellectual property protection ensures that the organization maintains ownership and control over its language technology.
6. Long-term Cost Savings: While building an internal LLM involves initial investment and ongoing maintenance costs, it can lead to long-term cost savings compared to relying on external models or APIs. Depending on the scale of usage, an organization may find it more cost-effective to develop and maintain its own LLM rather than paying for external services on a continuous basis.
7. Strategic Independence: By building your own LLM, organizations reduce their dependency on external providers for language capabilities. This strategic independence gives them the freedom to evolve the model, adapt to changing requirements, and innovate without being limited by the offerings or constraints of external models. It provides flexibility in terms of roadmap, feature development, and integration within the organization's ecosystem.

While building an internal LLM brings its own challenges and requires technical expertise, infrastructure, and ongoing maintenance, organizations see these advantages as significant drivers for pursuing in-house language model development. It allows them to have more control, customization, and strategic positioning in leveraging language technology for their specific needs.

What are the criteria which will help determine whether I build an LLM internally or use an external one?

Whether to build your own Language Model (LLM) or leverage an existing one like ChatGPT, will depend on several criteria. Here are some key factors to help you make an informed decision:

1. Expertise and Resources: Assess your organization's expertise and resources in natural language processing (NLP) and machine learning. Building your own LLM requires a dedicated team of researchers and engineers with expertise in NLP, deep learning, and model development. If you lack the necessary expertise, leveraging an existing LLM can be more efficient.
2. Time to Market: Consider your timeline for deployment. Building an LLM from scratch can be a time-consuming process that involves data collection, preprocessing, model training, fine-tuning, and validation. Leveraging an existing LLM can significantly reduce the time to market, allowing you to focus on other aspects of implementation.
3. Quality and Performance: Evaluate the quality and performance requirements for your specific use case. Existing LLMs like ChatGPT have undergone extensive training and validation, making them a reliable option for many applications. However, if your use case requires highly specialized domain knowledge or precise control over outputs, building a custom LLM might be necessary.
4. Data Availability: Consider the availability and quality of data specific to your use case. Training a high-quality LLM typically requires large amounts of diverse and relevant data. If you have access to a comprehensive dataset that is tailored to your needs, building your own LLM can be a viable option. Otherwise, leveraging an existing LLM with pre-trained models can provide a head start.
5. Cost Considerations: Evaluate the costs associated with building and maintaining an LLM internally. Building an LLM requires significant investments in research, infrastructure, and ongoing maintenance. Comparatively, leveraging an existing LLM often involves subscription fees or usage-based costs, which may be more cost-effective, especially for smaller organizations.
6. Scalability and Infrastructure: Consider the scalability and infrastructure requirements for your use case. Building and maintaining a scalable LLM infrastructure can be complex, requiring substantial computational resources and infrastructure management. Leveraging a pre-existing LLM allows you to benefit from the scalability and infrastructure provided by the model's host organization.
7. Innovation vs. Core Competencies: Assess whether developing an LLM aligns with your organization's core competencies and long-term strategy. If NLP and AI development are integral to your business and you anticipate ongoing innovation in this domain, building your own LLM can offer more control and flexibility. Conversely, leveraging an existing LLM allows you to focus on your core business while benefiting from state-of-the-art language models.
8. Ecosystem and Support: Consider the ecosystem and support available for both options. Building your own LLM may require you to establish extensive internal resources and expertise. In contrast, leveraging an existing LLM often provides access to a supportive community, developer tools, documentation, and ongoing updates and improvements from the model provider.

Ultimately, the decision to build your own LLM or leverage an existing one depends on a combination of factors including your organization's resources, expertise, time constraints, use case requirements, and long-term strategic goals. Assessing these criteria will help you make an informed choice that best aligns with your specific needs and objectives.

How do Internal and External LLMs compare?

Here's quick snapshot how each compares on several key attributes.

Can I get the best of both worlds by integrating my internal LLM with an external LLM?

Integrating your own Language Model (LLM) with an external model like ChatGPT can be a powerful approach to leverage the strengths of both models. Here are some steps to consider when integrating your LLM with an external model:

1. Identify Complementary Capabilities: Determine the specific capabilities and strengths of both your LLM and the external model. Understand the unique features or expertise that your LLM brings to the table and identify the areas where the external model, such as ChatGPT, excels. This analysis will help you identify opportunities for integration.
2. Define Integration Scenarios: Identify the specific scenarios or use cases where the integration of your LLM and ChatGPT would be beneficial. For example, you might want to use your LLM for specialized domain knowledge or to handle specific user intents, while leveraging ChatGPT for general conversational capabilities or generating creative responses.
3. Data Integration: Determine how you can integrate data from your LLM with ChatGPT. This may involve preprocessing your LLM's data to align with the input format of ChatGPT. You may need to convert or adapt your LLM's data to match the context or style of the external model, ensuring a seamless transition between the two.
4. API Integration: Explore the APIs or integration options provided by the external model, such as ChatGPT's API. Understand the input and output formats, rate limits, and any customization options available. Adapt your LLM to interact with the external model's API, allowing for the exchange of information and responses between the two models.
5. Orchestrating Model Outputs: Define how you will orchestrate the outputs of both models to provide a coherent response. You might consider using a rule-based system, reinforcement learning, or other techniques to combine or select the most appropriate responses from each model. This orchestration process will ensure a seamless and cohesive conversation experience.
6. Monitoring and Evaluation: Establish monitoring mechanisms to assess the performance and effectiveness of the integrated system. Continuously evaluate the outputs, measure user satisfaction, and collect feedback to fine-tune the integration. Monitor the models' performance individually as well as their combined performance to ensure quality and accuracy.
7. Iterative Improvement: Treat the integration as an iterative process, continuously improving and refining the integration based on user feedback and performance evaluation. Regularly assess the performance of both models and make adjustments to the integration strategy to optimize the user experience and achieve the desired outcomes.
8. Scalability and Infrastructure: Consider the scalability and infrastructure requirements of integrating your LLM with an external model. Ensure that your infrastructure can handle the combined computational demands of both models and any increased traffic resulting from the integration. Utilize cloud-based services or distributed systems to support the scalability requirements.
9. Legal and Ethical Considerations: Consider legal and ethical aspects, such as data privacy, ownership, and compliance with terms of service. Ensure that the integration and use of both models adhere to relevant regulations and ethical guidelines. Be transparent about the use of external models and provide clear disclosure to users regarding their participation in the conversation.

Integrating your LLM with an external model like ChatGPT requires careful planning, data integration, API usage, and iterative improvement. By combining the strengths of both models, you can create a more powerful and tailored conversational experience for your users while leveraging the benefits of the external model's capabilities.

Postscript

This is still an evolving field, so I am quite sure what I have shared above will continue to change and evolve as the capabilities of Generative AI evolve. Also, a lot depends on organization specific constraints and considerations. What works for one may not for other. Would love to hear from you about what plays in your mind and at your organization as you embark/progress on this journey of deploying Generative AI capabilities at your organization.

Stay Tuned. Stay Safe. Take Care.