ChatGPT is an LLM built on the Transformer architecture, which has enabled it to have an uncanny human-like intelligence in understanding the concepts behind sentences. This technology breakthrough has made ChatGPT one of the most advanced LLMs available, and understanding how ChatGPT works is crucial to unlocking its full potential. However, any inaccuracies and biases ChatGPT has are due to the data it was trained on. Moreover, LLMs still have current limitations such as limited memory and multimodal capabilities. Despite these shortcomings, organizations can integrate ChatGPT and other Transformer-based AI into their applications to improve their team’s productivity.
LLMs are evolving rapidly, and organizations that do not evaluate how this technology can impact their workflows are at risk of falling behind. Understanding how ChatGPT works is important to fully leverage its capabilities. Even industries that are slow to adopt new technologies can start leveraging low-cost tools to keep up with changing work practices. It may take a decade for knowledge work to be put on autopilot, so humans will still need to be “in the loop,” with LLMs increasingly playing a greater role as a copilot and productivity multiplier.
When using LLM-based applications, understanding how ChatGPT works is important to consider their wide-ranging effects, from privacy to impact on workflows. Fine-tuning LLMs helps them better understand an organization’s domain before integrating them into applications. Until there are further architectural breakthroughs, LLMs share the same fundamental shortcomings of AI models in general. However, with continued advancements and a careful approach, organizations can leverage LLMs, such as ChatGPT, to stay ahead of the curve in the rapidly evolving world of AI.
Included in this Intelligence Brief:
- Explanation of the breakthrough technology behind ChatGPT
- How ChatGPT works, including its base model and periodic testing
- Current shortcomings of Large Language Models (LLMs) like ChatGPT
- Importance of integrating LLMs like ChatGPT into applications for productivity
- Considerations for using LLM-based applications, including privacy and impact on workflows
- The need for fine-tuning LLMs to an organization’s domain for optimal use.
