In the swiftly changing digital world, businesses are progressively adopting advanced AI models, such as ChatGPT, to bolster their operations. These AI models are renowned for their capacity to express intricate ideas. However, they are predominantly trained on internet-based information and lack the capability to handle proprietary content or knowledge.
The capacity to utilize a company's proprietary knowledge is vital for its competitiveness and innovation, especially in today's dynamic environment. Knowledge within organizations is typically produced and captured across various sources and forms, making it a daunting task to organize and deploy effectively.
An increasing number of organizations are striving (experimenting with varied success) to harness the language processing skills and general reasoning abilities of large language models (LLMs) to capture and provide broad internal access to their intellectual capital. The technology to incorporate an organization’s specific domain knowledge into an LLM is advancing rapidly. Currently, there are three primary approaches: training an LLM from scratch, fine-tuning an existing LLM, and prompt-tuning an existing LLM:
Training an LLM from scratch demands a massive amount of high-quality data, considerable computing power, and data science talent. This approach is not common due to these requirements.
Fine-tuning an existing LLM involves adjusting some parameters of a base model and typically requires substantially fewer data and less computing time than creating a new model from scratch. However, it can still be expensive and necessitates considerable data science expertise.
Prompt-tuning an existing LLM is perhaps the most common approach for companies to kick off their AI journey. This approach involves modifying the original model through prompts in the context window that contains domain-specific knowledge. After prompt tuning, the model can answer questions related to that knowledge. This approach is the most computationally efficient of the three and does not require a vast amount of data to be trained on a new content domain, however, it has its own limitation such as word number limitations of the base models, etc.
To seize opportunities and manage potential risks of generative AI applications to knowledge management, companies need to foster a culture of transparency and accountability. Users need to comprehend how to safely and effectively incorporate generative AI capabilities into their tasks to enhance performance and productivity.
In conclusion, while there are many challenging issues involved in building and using generative AI systems trained on a company’s own knowledge content, the overall benefit to the company is worth the effort to address these challenges. The long-term vision of enabling any employee — and customers as well — to easily access important knowledge within and outside of a company to enhance productivity and innovation is a compelling draw. Generative AI appears to be the technology that is finally making it possible. Introducing Data Creative: Your AI Consulting Partner Are you ready to elevate your analytics capabilities with AI but unsure where to start? Data Creative is here to guide you! As a leading AI consulting firm, we specialize in helping companies harness the power of large language models and other AI technologies to supercharge their analytics. Our consulting services focus on: - Identifying the most relevant and impactful use cases for your business - Assessing the feasibility and return on investment (ROI) of AI projects - Recommending the best models, platforms, and tools for your specific needs - Developing strategies for fine-tuning and optimizing models - Providing guidance on data privacy, security, and ethical considerations By partnering with Data Creative, you'll not only benefit from our deep expertise in AI but also gain a strategic ally that understands your unique business challenges. We'll work closely with your team to tailor AI solutions that drive real results and help you stay ahead in today's fast-paced, data-driven world.
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