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Certification

Certification in Building Self-Improving AI Agents with Constitutional Feedback

Get certified in building self-improving AI agents who use tools and feedback to cut hallucinations, repair code in production, and ship safer, more reliable AI features.

Exam of 10 to 20 questionsCertificate for LinkedInIntermediate · Expert, technical
IT and developmentProduct development
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The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

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What the exam covers

2 questions from each of the 10 chapters of the course

20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01Building on LLMs1:47
  2. 02ReAct8:33
  3. 03ReAct on grounded tasks13:00
  4. 04ReAct's challenges12:40
  5. 05RLEF: execution feedback for code5:27
  6. 06RLEF deep dive4:59
  7. 07Constitutional AI6:51
  8. 08Constitutional AI results12:38
  9. 09Experience versus reasoning1:08
  10. 10Summary and takeaways4:10
About the course

This certification, built around Stanford's CS329A video course, takes you from static LLMs to self-improving AI agents that actually think, act, and get better over time. You'll pick up hands-on skills in tool integration, execution feedback, and constitutional design,which translates into fewer hallucinations, faster debugging, and sharper decision-making, not to mention a serious competitive advantage and a future-proof career. If you're ready to build AI that learns from its own mistakes and earns trust in real-world systems, this is the course for you.

This certification covers the following topics:

  • Building autonomous agents from base LLMs
  • Connecting agents to external tools and APIs
  • Execution feedback loops for self-correction
  • Constitutional AI principles and behavior guardrails
  • Reducing hallucinations through iterative reflection
  • Automated debugging and code repair with agents
  • Designing safe multi-step reasoning workflows
  • Evaluating agent performance and reliability
  • Prompt engineering for agentic behavior
  • Deployment strategies for production-ready agents