Human judgment in the agent improvement loop
📰 LangChain Blog
Learn how to incorporate human judgment into the agent improvement loop to enhance AI agents with your team's knowledge and expertise
Action Steps
- Identify the tacit knowledge within your team that can be used to improve AI agents
- Document and formalize the institutional knowledge that is already available
- Develop a feedback loop to incorporate human judgment into the agent improvement process
- Train AI agents using the documented knowledge and feedback from human evaluators
- Test and refine the AI agents to ensure they reflect the team's knowledge and judgment
Who Needs to Know This
Product managers, AI engineers, and data scientists can benefit from this knowledge to improve their AI agents and make them more effective in reflecting the team's expertise
Key Insight
💡 Incorporating human judgment into the agent improvement loop is crucial to make AI agents more effective and reflective of the team's knowledge and expertise
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🤖 Enhance your AI agents with human judgment to reflect your team's expertise! #AI #AgentImprovement
Key Takeaways
Learn how to incorporate human judgment into the agent improvement loop to enhance AI agents with your team's knowledge and expertise
Full Article
AI agents work best when they reflect the knowledge and judgment your team has built over time. Some of that is institutional knowledge that’s already documented and easy for an agent to use as-is. But most great organizations also rely on tacit knowledge that lives inside their employees’ minds.
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