The article emphasizes the importance of carefully choosing the right large language model (LLM) for specific tasks, as not all models have equal capabilities. The author shares lessons learned from using LLMs, noting that they should not be expected to handle all decision-making tasks independently. It's more effective to let applications manage logic and utilize LLMs for content generation. Additionally, the article advocates for assigning singular responsibilities to different models to avoid confusion and enhance productivity, highlighting the unique strengths of models like OpenAI's GPT-4, Claude, and Gemini.
Trying to make a single LLM do multiple jobs is a recipe for confusion. It's better to give each agent one responsibility to avoid complexity.
Don't use one model for everything. Be intentional. Try, test, and pick the best one for your use case. Every model has a unique strength.
Let your app handle the logic. Use LLMs to generate things, not to decide everything. They're great at language; not always great at logic.
Some models have way more knowledge about certain locations or domains. Use OpenAI GPT-4 for deep reasoning tasks, and Claude or Gemini for other areas.
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