🚀 Keep Prompts Focused

Overloading instructions is a common pitfall. Asking LLMs to do too much in one go can confuse the model and lead to subpar outputs. Instead, break tasks into clear, manageable steps for better results.

Here’s how it works in Python scripting 👇

🚀 Exciting news for AI enthusiasts! Check out my LLM Prompts repository on GitHub! This collection features a variety of prompts designed for use with Large Language Models (LLMs).

🔗 Explore the repository here: github.com/spsanderson/...

#AI #MachineLearning #LLM #OpenSource #GitHub

🔑 Give LLMs a Way Out!

When crafting prompts, ensure the model knows what to do if it doesn’t understand something. Without this, it might force an answer, leading to errors or confusion.

Eg: "If unclear, say 'I don’t know' or ask clarifying questions."

🧠 #LLM #PromptEngineering #AI

🚫 Don’t Let Examples Anchor Your Outputs!

When you provide examples with concrete details—like numbers, names, or specific outcomes—the model tends to mirror or over-rely on those elements.

Eg: If your prompt has "10M," "Company X," or "20%," it may use these in the output 😬 #LLM #ChatGPT