Unleashing AI Coding Agents: Enhancing Human Judgment Through Memory
In the evolving landscape of AI coding agents, a common challenge arises: repetitive engineering principles. Despite the use of prompts and rules, issues with memory and context persist.
Key insights from my recent experiments include:
- Prompts Disappear: Once a task is completed, so are the prompts.
- Rules Limit Context: They often apply in narrow scenarios, failing to capture broader principles.
- Personal Preferences: Some principles shouldn’t be enforced project-wide; they can vary by individual.
To address this, I introduced a “memory” layer, capturing small, relevant knowledge snippets. This approach highlights key realizations:
- Vague memory breeds vague behavior.
- Excessive long-term memory can clutter context.
- Duplicate entries complicate knowledge retrieval.
AI excels when context aligns; however, human intuition remains vital for decisions about what to remember and prioritize.
How are you navigating this challenge? Are prompts, rules, or persistent knowledge your go-to? Share your thoughts!
