Entrepreneur | Innovating at the Intersection of Technology, Market Access, Reimbursement, and Evidence Generation
🔨 "When You Have an LLM Hammer, Not Everything Is a Nail" In our excitement to harness the power of Large Language Models (LLMs), it’s easy to fall into the trap of treating every task as if it can be solved with this shiny new "hammer." However, not all problems are best tackled this way. LLMs, being probabilistic machines, interpret tasks based on numerous factors that can introduce variability, even if the instructions are crystal clear. For instance, filtering a large database might be done more efficiently with a simple code snippet rather than asking an LLM to identify key data points, where it might miss critical parameters. Relying on LLMs for every step of a project can soon become a more complex and labor-intensive process than using more straightforward, traditional tools. The key is not to abandon traditional approaches as a whole but to find ways to integrate LLMs as an additional tool in our arsenal. This balanced approach means evaluating workflows to identify where LLMs can genuinely add value and where conventional methods perform just as well or even better. Such thoughtful integration minimizes unnecessary complications, ensures more stable outcomes, and helps avoid the frustrations that come with over-reliance. By leveraging LLMs alongside proven methods, we can achieve a more efficient and effective process overall. Think carefully about your "process" and you will identify best possible use cases for the LLMs. #LLM #GenAI #HEOR #Process
