The article discusses the limitations of Large Language Models (LLMs) in maintaining context and memory, and proposes techniques like message numbering to enhance their performance during interactions.
Making LLMs Remember using this Prompting Technique
The article discusses the limitations of Large Language Models (LLMs) in maintaining context and memory, and proposes techniques like message numbering to enhance their performance during interactions.
Large Language Models (LLMs) are very powerful, but they are inherently stateless. They don’t have a continuous memory like humans do. This leads to challenges when you need them to consistently:
- Remember rules
- Maintain context during long conversations
- Perform actions at specific intervals
When you’re coding or engaged in extended conversations with Claude (or other LLMs), one thing becomes clear: they forget.
They don’t just forget in coding sessions; even in lengthy scenarios, the forgetting can occur very early—sometimes right after a simple “Hello World” and a few small modifications.
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The Problem: Inconsistent Memory in LLMs
I really enjoy coding with Claude Sonnet because it saves a significant amount of time. However, over time, it tends to break things apart, forget important details, add unnecessary changes, and sometimes even overcomplicates code—reducing the quality of what was once beautiful code.
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