Fine-Tune Your LLMs or Face AI Failure
In the wake of the chaos at OpenAI, a resolution was swiftly reached, although it was not a foregone conclusion. The situation required decisive intervention from Microsoft CEO Satya Nadella to steer things back to stability.
This scenario has highlighted the risks associated with relying exclusively on a single generative AI provider. Many clients, as a result, are experiencing a sense of vulnerability and uncertainty regarding their position in an environment that is constantly evolving.
This raises important questions: What is the best strategy for effectively using generative AI? Furthermore, how can clients take more control in this crucial technological field?
The answers lie in having a deeper engagement in the development and fine-tuning of large language models (LLMs). This approach transcends the conventional method of merely purchasing an off-the-shelf product, especially in areas where strategic interests are at stake. By actively participating in the development phase, businesses not only gain a better understanding of the technology but also ensure that the end product is tailored to meet their specific needs and goals. Essentially, the best strategy is a “buy and build” approach to generative AI.
When to Buy
Understanding the intricacies of generative AI technology poses a significant challenge, particularly considering its early stage of development and the rapid pace of innovation in the field. The landscape is dynamic, with new advancements emerging frequently, making it a challenge even for experts to stay abreast of all developments.
When it comes to employing foundational models in generative AI, opting for ready-made solutions is often a wise choice. Building these complex systems internally entails substantial costs and resource commitments. It requires assembling a team of skilled data scientists for development, extensive datasets for training, and thorough testing protocols. Moreover, the infrastructure demands, like procuring GPUs, further escalate the cost and complexity. This has been worsened by the limited availability of these critical components.
For foundational models, a mix of proprietary and open-source platforms could be an effective strategy. Proprietary models from entities like OpenAI or Anthropic offer cutting-edge technology, while open-source platforms, such as Meta’s Llama2, provide benefits like greater customization and transparency. These open-source models are rapidly advancing and are beginning to parallel their proprietary counterparts in sophistication.
In cases where scale is a key factor, sourcing or partnering for a best-of-breed solution becomes a viable and cost-effective alternative for generative AI. For instance, creating a detailed database for specific intents can be an arduous and expensive task, making purpose-built third-party solutions more appealing.
Furthermore, purchasing specialized LLMs might be necessary for domains beyond an organization’s primary expertise, such as customer service, sales and marketing, procurement, human resources, and supply chain management. Developing custom solutions for these areas could be inefficient and could divert resources away from more strategic organizational goals. Leveraging third-party expertise in these situations allows for a more focused and effective use of resources.
Buy and Build
After securing the key elements of generative AI systems, the focus should shift towards crafting tailored solutions. This stage demands careful planning and the backing of senior leadership to ensure access to required resources and proper prioritization. Setting up a Center of Excellence can play a pivotal role here, offering strategic guidance, administration, and maintaining the drive of the generative AI project.
The true value and transformative potential of generative AI emerge when businesses build upon these foundational models, effectively grounding them in their unique datasets. This involves integrating proprietary company data into the core models, tailoring them to specific organizational contexts and needs.
A prime example of this application is the fine-tuning of models with internal data such as customer service tickets, conversation logs, and knowledge bases. This customization leads to a deeper understanding of customer needs and significantly improves the responsiveness and relevance of answers provided by the AI. Moreover, this integration allows the system to establish a feedback loop, where it learns from recurring issues and autonomously generates useful content like FAQs or knowledge base articles, directly addressing customer concerns.
Beyond reactive responses, an AI system can proactively anticipate user needs. For instance, the AI could predict the need for different software when an employee changes roles, automatically initiating the provisioning process. In a retail environment, AI might analyze purchasing patterns and predict inventory needs before they become critical, or in a healthcare setting, it could anticipate patient needs based on historical health data and recent interactions.
These capabilities not only streamline processes but also lead to significant cost reductions and enhanced customer satisfaction. However, the dynamism of AI is a double-edged sword. Its effectiveness hinges on its ability to evolve continuously. Operationalizing AI models for ongoing learning, maintenance, and support is critical. It’s not enough to set up and deploy these systems. It’s essential to monitor and test their outcomes regularly. Are the AI-generated solutions improving over time? Is the system adapting effectively to new data and evolving user needs? Continuous assessment and fine-tuning are necessary to ensure the AI remains a valuable asset rather than becoming obsolete.
Conclusion
The events surrounding OpenAI underscore the necessity for enterprises to adopt a strategic “buy and build” approach to generative AI. While leveraging off-the-shelf foundational models for their advanced capabilities and efficiency is prudent, the real value for businesses lies in fine-tuning these models to align with their unique needs and goals. This approach requires the involvement of senior leadership and the establishment of a Center of Excellence to drive the initiative.
This balanced strategy not only provides a competitive edge but also guards against the vulnerabilities of depending on a single AI provider. It emphasizes the necessity of a proactive, hands-on engagement in AI development, ensuring that these powerful tools remain relevant, effective, and aligned with the dynamic needs of the enterprise.
About the Author
Muddu Sudhakar is a successful Entrepreneur, Executive, and Investor. Muddu has deep Product, technology, and GTM experience and knowledge of enterprise markets such as Cloud, SaaS, AI/Machine learning, IoT, Cybersecurity, Big Data, Storage, and chip/Semiconductors. Muddu has strong operating experience with startups as CEO (Caspida, Cetas, Kazeon, Sanera, Rio Design) and in public companies as SVP & GM role at likes of ServiceNow, Splunk, VMware, and EMC. Muddu has founded 5 startups, and all of them are successfully acquired and provided 10x returns for shareholders & investors. His latest startup, Aisera, has attracted funding from top-tier investors like Webb Investment Network, World Innovation Lab (WiL), True Ventures, and Thoma Bravo.
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