The article also points out potential issues with LLMs, such as their high cost, computational expense, and potential for bias in their outputs. It recommends considering alternative technologies in cases where accuracy, objectivity, speed, or resource preservation are critical, or where fairness and unbiased decision-making are crucial. The author concludes that while generative AI is a valuable tool for analyzing large amounts of data and driving business efficiencies, it is not a one-size-fits-all solution.
Key takeaways:
- Generative AI, particularly large language models (LLMs), can be highly effective for tasks such as writing follow-up emails, rephrasing content, and summarizing key points from emails or calls.
- However, the accuracy of these models can drop significantly when tasked with more specific outcomes. To achieve higher accuracy, enterprises may need specialized models trained on a specific domain or use case.
- Large-scale LLMs may not be the best choice for business cases where accuracy, objectivity, speed, or resource preservation are critical. They can pose risks in high-stakes applications, real-time applications, and situations requiring safeguarding against bias and fairness concerns.
- While generative AI is a useful tool for analyzing large amounts of data and driving business efficiencies, there are use cases for which other technology tools may be a better match.