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Agentic RAG

Test Your Skills

Explore your understanding of agentic RAG by applying theoretical knowledge to practical design, coding, and debugging challenges. This lesson guides you through assessing your grasp of AI agent design, multi-tool integration, and troubleshooting, helping you build confidence in crafting reliable agentic systems.

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Welcome to our final lesson. Throughout this course, we have journeyed from core theory to practical implementation, debugging, and advanced architectural design. This lesson is your opportunity to put all that knowledge to the test.

The following exercises are structured to assess your understanding at every level, from high-level concepts to hands-on coding and debugging. Detailed solutions are provided at the end for self-assessment. Good luck!

Part 1: Conceptual foundations

Here, we test our recall and understanding of the core theoretical concepts that underpin agentic RAG.

What are the four core components of an agent, and what is the primary role of each in an Agentic RAG system?
Test your recall of the four essential components that make up an agent

To view solution, click the “Show answer” button.

The ReAct Framework: In the Thought -> Act -> Observe cycle, what is the purpose of the “Observe” step, and how does it enable an agent to be more adaptable than a static pipeline?
Explain the critical role of the ‘Observe’ step in the ReAct reasoning cycle

To view solution, click the “Show answer” button.

Why is a clear and descriptive (rather than instructional) description for a tool the most critical element for ensuring an agent uses it correctly?
Describe why a tool’s description is vital for accurate agent tool selection

If you’re stuck, click the “Show solution” button.

Why is a metric like faithfulness more useful for evaluating a RAG system’s output than a traditional NLP metric like BLEU score?
Compare the RAG evaluation metric ‘faithfulness’ against the traditional BLEU score

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