Assessing the Practical Utility of Large Language Models
The author discusses use cases for large language models (LLMs) that provide genuine utility by reducing friction on tasks the user already values, rather than just hype or theoretical future applications.
Why it matters
Identifying practical, utility-driven use cases for LLMs can help cut through the hype and demonstrate the real-world impact of this technology.
Key Points
- 1The author is looking for LLM use cases that address tasks the user already does, but with high enough friction that they rarely do them
- 2Examples of useful LLM applications include automating note-taking and formatting, not just writing emails
- 3The author wants honest assessments of where LLMs have failed or disappointed, not just praise
Details
The author is building a space to discuss practical, utility-driven use cases for large language models (LLMs). The key criteria are that the tasks should be things the user already values and does, but with enough friction that they often skip them. For example, the author uses an LLM to quickly convert handwritten math notes into a clean, structured PDF - a task they could do manually but rarely did due to the effort involved. The author is not interested in hype, theoretical future applications, or one-off use cases. Instead, they want specific workflows with time estimates, as well as honest assessments of where the LLM technology has fallen short. The goal is to identify LLM use cases that genuinely save the user time and effort on tasks they already find valuable.
No comments yet
Be the first to comment