Two Knowledge Hierarchies: Structuring Context for AI Agents and LLMs
The article discusses the development of TestSmith, focusing on two distinct knowledge hierarchies for AI agents and LLMs. It emphasizes the need for tailored context to improve efficiency in code generation and debugging. The proposed solution involves structured context files that provide relevant information without overwhelming the AI agents.
- ▪TestSmith serves two audiences: AI agents for code development and LLMs for generating test code.
- ▪A CLAUDE.md hierarchy is used to provide specific context to AI agents based on their tasks.
- ▪The runtime context for LLMs is managed through a conventions file that integrates project-wide and package-level settings.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 409515) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Oscar Rieken Posted on May 27 Two Knowledge Hierarchies: Structuring Context for AI Agents and LLMs #ai #claudeai #llm #devtools Building TestSmith (6 Part Series) 1 Why We Built TestSmith: The Test Coverage Problem Nobody Talks About 2 Why We Rewrote Our Python CLI in Go (and What We Gained) ... 2 more parts...
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