Common Anti-Patterns to Avoid When Working with Large Language Models
By
mkagenius
Sesame, salt, and substance. A flagship bake.
Summary
The article discusses common anti-patterns to avoid when working with Large Language Models (LLMs), based on 15 months of experience. It identifies problematic practices such as sending redundant context information, over-engineering prompts, and inefficient use of context windows. The author emphasizes that context is a scarce resource that should be used wisely, and provides practical examples of behaviors to avoid for more effective LLM interactions.
Key quotes
· 4 pulledContext is a scarce resource and probably worth its weight in gold, we need to use it wisely.
By anti-patterns, I simply mean patterns or behaviors we should avoid when working with LLMs.
One of the learnings is to not send the same information/text multiple times in the same session.
Anti-patterns observed while working extensively with LLMs — from redundant context to over‑engineering.
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