> ## Documentation Index
> Fetch the complete documentation index at: https://prompt.university/llms.txt
> Use this file to discover all available pages before exploring further.

# Module 3 Assessment

> Build a Multi-Capability AI Assistant using advanced prompting techniques

<Info>
  **Time Estimate:** 60-90 minutes\
  **Passing Score:** 80% (20/25 questions + successful project implementation)
</Info>

## Assessment Overview

This assessment evaluates your mastery of advanced prompting techniques covered in Module 3:

* Chain of Thought (CoT) prompting
* Problem decomposition strategies
* Self-refinement and iteration
* Ensembling and multi-path reasoning
* Tool integration and RAG

<Note>
  This is the culminating assessment for the entire Prompt University course. It combines techniques from all three modules into a comprehensive final project.
</Note>

***

## Part 1: Multiple Choice Questions (25 questions)

### Question 1: Chain of Thought Fundamentals

What is the primary mechanism by which CoT improves reasoning accuracy?

<Tabs>
  <Tab title="Your Answer">
    A) It makes the model run slower, allowing more processing time\
    B) It breaks down complex problems into explicit intermediate steps\
    C) It increases the model's parameter count\
    D) It accesses external knowledge bases
  </Tab>

  <Tab title="Correct Answer">
    **B) It breaks down complex problems into explicit intermediate steps**

    **Explanation:** CoT works by making the reasoning process explicit, allowing the model to decompose problems, track intermediate results, and catch errors mid-reasoning.
  </Tab>
</Tabs>

***

### Question 2: Zero-Shot CoT

Which phrase is most effective for triggering zero-shot Chain of Thought reasoning?

<Tabs>
  <Tab title="Your Answer">
    A) "Think carefully"\
    B) "Let's think step-by-step"\
    C) "Use your knowledge"\
    D) "Be thorough"
  </Tab>

  <Tab title="Correct Answer">
    **B) "Let's think step-by-step"**

    **Explanation:** Research shows "Let's think step-by-step" is the most consistently effective phrase for activating CoT reasoning across different models and tasks.
  </Tab>
</Tabs>

***

### Question 3: When to Use CoT

When is Chain of Thought prompting LEAST beneficial?

<Tabs>
  <Tab title="Your Answer">
    A) Multi-step math problems\
    B) Simple factual questions\
    C) Logical reasoning tasks\
    D) Counter-intuitive problems
  </Tab>

  <Tab title="Correct Answer">
    **B) Simple factual questions**

    **Explanation:** CoT adds overhead without benefit for simple questions like "What is the capital of France?" Use it for problems requiring 2+ reasoning steps.
  </Tab>
</Tabs>

***

### Question 4: Least-to-Most Prompting

What is the key principle of least-to-most prompting?

<Tabs>
  <Tab title="Your Answer">
    A) Start with the hardest sub-problem first\
    B) Solve the simplest sub-problem first and build up\
    C) Solve all sub-problems simultaneously\
    D) Skip intermediate steps
  </Tab>

  <Tab title="Correct Answer">
    **B) Solve the simplest sub-problem first and build up**

    **Explanation:** Least-to-most prompting starts with the easiest sub-problem and progressively builds to more complex ones, using earlier solutions to inform later ones.
  </Tab>
</Tabs>

***

### Question 5: Problem Decomposition

When should you use problem decomposition?

<Tabs>
  <Tab title="Your Answer">
    A) For all problems, regardless of complexity\
    B) Only for math problems\
    C) For hierarchical or multi-domain problems\
    D) Never, it's always inefficient
  </Tab>

  <Tab title="Correct Answer">
    **C) For hierarchical or multi-domain problems**

    **Explanation:** Decomposition is most valuable for problems with natural sub-components, multiple domains, or sequential dependencies.
  </Tab>
</Tabs>

***

### Question 6: Self-Consistency

How does self-consistency improve accuracy?

<Tabs>
  <Tab title="Your Answer">
    A) By generating multiple reasoning paths and using majority vote\
    B) By making the model more confident\
    C) By increasing temperature\
    D) By using longer prompts
  </Tab>

  <Tab title="Correct Answer">
    **A) By generating multiple reasoning paths and using majority vote**

    **Explanation:** Self-consistency generates multiple independent solutions and selects the most common answer, reducing the impact of individual errors.
  </Tab>
</Tabs>

***

### Question 7: Iterative Refinement

What is the correct order for the refinement cycle?

<Tabs>
  <Tab title="Your Answer">
    A) Critique → Generate → Refine\
    B) Generate → Refine → Critique\
    C) Generate → Critique → Refine\
    D) Refine → Generate → Critique
  </Tab>

  <Tab title="Correct Answer">
    **C) Generate → Critique → Refine**

    **Explanation:** The refinement cycle starts with generation, then critiques the output, then generates an improved version based on the critique.
  </Tab>
</Tabs>

***

### Question 8: Self-Critique

What makes a good self-critique?

<Tabs>
  <Tab title="Your Answer">
    A) Vague statements like "could be better"\
    B) Specific, actionable issues with concrete examples\
    C) Only positive feedback\
    D) Focus on style over substance
  </Tab>

  <Tab title="Correct Answer">
    **B) Specific, actionable issues with concrete examples**

    **Explanation:** Effective critiques identify specific problems with clear examples, making it possible to generate targeted improvements.
  </Tab>
</Tabs>

***

### Question 9: Ensembling Benefits

What is the primary advantage of ensembling multiple approaches?

<Tabs>
  <Tab title="Your Answer">
    A) It's faster than single approaches\
    B) Different approaches make different errors, reducing overall error rate\
    C) It uses less computational resources\
    D) It's simpler to implement
  </Tab>

  <Tab title="Correct Answer">
    **B) Different approaches make different errors, reducing overall error rate**

    **Explanation:** Ensembling leverages diversity—different methods have different blind spots, so combining them creates more robust solutions.
  </Tab>
</Tabs>

***

### Question 10: Voting Mechanisms

When should you use weighted voting instead of simple majority vote?

<Tabs>
  <Tab title="Your Answer">
    A) Always, it's always better\
    B) When some methods are more reliable for the specific problem type\
    C) Never, simple majority is always sufficient\
    D) Only for math problems
  </Tab>

  <Tab title="Correct Answer">
    **B) When some methods are more reliable for the specific problem type**

    **Explanation:** Weighted voting makes sense when you have evidence that certain methods perform better for specific problem types.
  </Tab>
</Tabs>

***

### Question 11: RAG Fundamentals

What does RAG stand for and what does it do?

<Tabs>
  <Tab title="Your Answer">
    A) Random Answer Generation - generates random responses\
    B) Retrieval-Augmented Generation - retrieves relevant info before generating\
    C) Rapid AI Generation - speeds up response time\
    D) Recursive Algorithm Generation - creates algorithms recursively
  </Tab>

  <Tab title="Correct Answer">
    **B) Retrieval-Augmented Generation - retrieves relevant info before generating**

    **Explanation:** RAG retrieves relevant information from a knowledge base and includes it in the prompt context before generating an answer.
  </Tab>
</Tabs>

***

### Question 12: RAG Benefits

What is the primary benefit of using RAG?

<Tabs>
  <Tab title="Your Answer">
    A) Faster response times\
    B) Reduced hallucinations through factual grounding\
    C) Smaller model size requirements\
    D) Simpler prompts
  </Tab>

  <Tab title="Correct Answer">
    **B) Reduced hallucinations through factual grounding**

    **Explanation:** RAG grounds responses in retrieved factual information, dramatically reducing hallucinations and improving accuracy.
  </Tab>
</Tabs>

***

### Question 13: Knowledge Grounding

What is the most important rule for knowledge-grounded responses?

<Tabs>
  <Tab title="Your Answer">
    A) Always provide an answer, even if uncertain\
    B) Answer ONLY based on provided context, never infer\
    C) Make educated guesses when information is missing\
    D) Prioritize creativity over accuracy
  </Tab>

  <Tab title="Correct Answer">
    **B) Answer ONLY based on provided context, never infer**

    **Explanation:** Strict grounding rules prevent hallucinations by ensuring responses are based solely on verified information.
  </Tab>
</Tabs>

***

### Question 14: Source Citation

Why is source citation important in RAG systems?

<Tabs>
  <Tab title="Your Answer">
    A) It makes responses longer\
    B) It provides verifiability and traceability\
    C) It's required by law\
    D) It impresses users
  </Tab>

  <Tab title="Correct Answer">
    **B) It provides verifiability and traceability**

    **Explanation:** Citations allow users to verify information and trace it back to original sources, building trust and enabling fact-checking.
  </Tab>
</Tabs>

***

### Question 15: Tool Integration

What is the main purpose of integrating external tools with LLMs?

<Tabs>
  <Tab title="Your Answer">
    A) To make the system more complex\
    B) To enable actions and access to real-time/specialized data\
    C) To slow down response time\
    D) To increase token usage
  </Tab>

  <Tab title="Correct Answer">
    **B) To enable actions and access to real-time/specialized data**

    **Explanation:** Tool integration extends LLM capabilities beyond text generation to include calculations, API calls, database queries, and real-world actions.
  </Tab>
</Tabs>

***

### Question 16: CoT Accuracy Improvement

By approximately how much can CoT improve accuracy on reasoning tasks?

<Tabs>
  <Tab title="Your Answer">
    A) 5-10%\
    B) 15-20%\
    C) 30-50%\
    D) 70-90%
  </Tab>

  <Tab title="Correct Answer">
    **C) 30-50%**

    **Explanation:** Research shows CoT can improve accuracy by 30-50% on complex reasoning tasks, with some benchmarks showing even larger gains.
  </Tab>
</Tabs>

***

### Question 17: Decomposition vs CoT

How does problem decomposition differ from Chain of Thought?

<Tabs>
  <Tab title="Your Answer">
    A) They're the same technique\
    B) Decomposition breaks into sub-problems; CoT shows step-by-step reasoning\
    C) CoT is always better\
    D) Decomposition is only for math
  </Tab>

  <Tab title="Correct Answer">
    **B) Decomposition breaks into sub-problems; CoT shows step-by-step reasoning**

    **Explanation:** Decomposition explicitly breaks problems into separate sub-problems, while CoT shows reasoning steps within a single problem-solving process.
  </Tab>
</Tabs>

***

### Question 18: Refinement Iterations

How many refinement iterations are typically recommended for critical content?

<Tabs>
  <Tab title="Your Answer">
    A) Always just 1\
    B) 2-3 iterations\
    C) 10+ iterations\
    D) Never refine, first draft is best
  </Tab>

  <Tab title="Correct Answer">
    **B) 2-3 iterations**

    **Explanation:** For important outputs, 2-3 refinement iterations typically provide the best balance between quality improvement and resource investment.
  </Tab>
</Tabs>

***

### Question 19: Ensembling Cost

What is the main trade-off when using ensembling?

<Tabs>
  <Tab title="Your Answer">
    A) Reduced accuracy for faster speed\
    B) Increased computational cost for improved accuracy\
    C) Simpler implementation for reduced features\
    D) No trade-offs, it's always better
  </Tab>

  <Tab title="Correct Answer">
    **B) Increased computational cost for improved accuracy**

    **Explanation:** Ensembling requires multiple model calls, increasing cost and latency, but provides more robust and accurate results.
  </Tab>
</Tabs>

***

### Question 20: RAG vs Fine-Tuning

When should you use RAG instead of fine-tuning?

<Tabs>
  <Tab title="Your Answer">
    A) When information changes frequently\
    B) When you want to modify model behavior permanently\
    C) When you have unlimited training data\
    D) Never, fine-tuning is always better
  </Tab>

  <Tab title="Correct Answer">
    **A) When information changes frequently**

    **Explanation:** RAG is ideal for dynamic information that changes frequently, as it can be updated without retraining. Fine-tuning is better for stable behavioral changes.
  </Tab>
</Tabs>

***

### Question 21: Verification in CoT

Why is adding a verification step important in CoT?

<Tabs>
  <Tab title="Your Answer">
    A) It makes the response longer\
    B) It catches errors in reasoning before finalizing the answer\
    C) It's required by the model\
    D) It impresses users
  </Tab>

  <Tab title="Correct Answer">
    **B) It catches errors in reasoning before finalizing the answer**

    **Explanation:** Verification steps help identify calculation errors or logical flaws before presenting the final answer, improving accuracy.
  </Tab>
</Tabs>

***

### Question 22: Recursive Decomposition

When is recursive decomposition most useful?

<Tabs>
  <Tab title="Your Answer">
    A) For all problems\
    B) For self-similar problems where sub-problems resemble the main problem\
    C) Only for computer science problems\
    D) Never, it's too complex
  </Tab>

  <Tab title="Correct Answer">
    **B) For self-similar problems where sub-problems resemble the main problem**

    **Explanation:** Recursive decomposition works best when sub-problems have the same structure as the main problem, allowing the same solution approach.
  </Tab>
</Tabs>

***

### Question 23: Confidence Scoring

What should you do when multiple solutions have low confidence?

<Tabs>
  <Tab title="Your Answer">
    A) Pick one randomly\
    B) Add more solution methods or request more information\
    C) Always go with the first solution\
    D) Give up
  </Tab>

  <Tab title="Correct Answer">
    **B) Add more solution methods or request more information**

    **Explanation:** Low confidence across methods indicates uncertainty. Adding more approaches or gathering more information can improve confidence.
  </Tab>
</Tabs>

***

### Question 24: Tool Error Handling

What's the best approach when a tool call fails?

<Tabs>
  <Tab title="Your Answer">
    A) Give up immediately\
    B) Try alternative tools or approaches, explain the limitation to the user\
    C) Pretend it worked\
    D) Ignore the error
  </Tab>

  <Tab title="Correct Answer">
    **B) Try alternative tools or approaches, explain the limitation to the user**

    **Explanation:** Graceful error handling includes trying alternatives and transparently communicating limitations to maintain user trust.
  </Tab>
</Tabs>

***

### Question 25: Combining Techniques

Which combination of techniques is most powerful for complex, high-stakes problems?

<Tabs>
  <Tab title="Your Answer">
    A) Just use CoT alone\
    B) CoT + Decomposition + Self-Refinement + RAG\
    C) Only use RAG\
    D) Avoid combining techniques
  </Tab>

  <Tab title="Correct Answer">
    **B) CoT + Decomposition + Self-Refinement + RAG**

    **Explanation:** Combining multiple advanced techniques provides the most robust solution for complex, high-stakes problems where accuracy is critical.
  </Tab>
</Tabs>

***

## Part 2: Final Project - Multi-Capability AI Assistant

<Warning>
  **Capstone Project:** This comprehensive project tests your ability to integrate ALL advanced techniques from Module 3.
</Warning>

### Project Overview

Build a **Multi-Capability AI Assistant** that:

1. Uses **Chain of Thought** for reasoning
2. Applies **problem decomposition** for complex queries
3. Implements **self-refinement** for quality assurance
4. Leverages **RAG** for factual grounding
5. Integrates **external tools** for calculations and data access

### System Requirements

Your assistant must handle three types of queries:

**Type 1: Complex Reasoning** (CoT + Decomposition)\
**Type 2: Knowledge-Intensive** (RAG + Source Citation)\
**Type 3: Action-Requiring** (Tool Integration)

***

### Task 1: Complex Reasoning System (30 points)

Build a system that handles multi-step reasoning problems.

**Requirements:**

* Use Chain of Thought for step-by-step reasoning
* Apply decomposition for problems with 3+ sub-components
* Include verification steps
* Show confidence levels

<Accordion title="System Prompt Template">
  ```
  You are an advanced reasoning assistant that solves complex problems systematically.

  Problem: [USER QUERY]

  STEP 1: Analyze Complexity
  - Is this a single-step or multi-step problem?
  - Does it require decomposition?
  - What's the core challenge?

  STEP 2: Choose Strategy
  - Simple problem: Direct CoT
  - Complex problem: Decomposition + CoT
  - Ambiguous problem: Multiple approaches

  STEP 3: Solve with CoT
  Let's think step-by-step:
  [DETAILED REASONING]

  STEP 4: Verify Solution
  Check:
  - Are all steps logical?
  - Does the answer make sense?
  - Any calculation errors?

  STEP 5: Present Solution
  Answer: [FINAL ANSWER]
  Confidence: [HIGH/MEDIUM/LOW]
  Reasoning: [SUMMARY]
  ```
</Accordion>

**Test Case:**
"A company wants to reduce costs by 20% while maintaining quality. They spend $500K on materials, $300K on labor, and \$200K on overhead. What strategies should they consider?"

<Accordion title="Expected Output Structure">
  ```
  STEP 1: Analyze Complexity
  This is a multi-faceted business problem requiring:
  - Cost analysis across categories
  - Strategic recommendations
  - Trade-off considerations

  STEP 2: Decomposition
  Sub-problem 1: Calculate target cost reduction ($200K)
  Sub-problem 2: Analyze each cost category
  Sub-problem 3: Identify reduction strategies
  Sub-problem 4: Assess quality impact

  STEP 3: Solve Each Sub-Problem

  Sub-problem 1: Target Reduction
  - Current total: $1M
  - Target reduction: 20% = $200K
  - New target: $800K

  Sub-problem 2: Category Analysis
  - Materials: $500K (50%)
  - Labor: $300K (30%)
  - Overhead: $200K (20%)

  Sub-problem 3: Strategies by Category

  Materials ($500K → target $400K):
  - Negotiate bulk discounts: -10% = $50K
  - Alternative suppliers: -5% = $25K
  - Reduce waste: -5% = $25K
  Total potential: $100K

  Labor ($300K → target $240K):
  - Process automation: -10% = $30K
  - Efficiency improvements: -5% = $15K
  - Flexible scheduling: -5% = $15K
  Total potential: $60K

  Overhead ($200K → target $160K):
  - Renegotiate contracts: -10% = $20K
  - Energy efficiency: -5% = $10K
  - Space optimization: -5% = $10K
  Total potential: $40K

  Sub-problem 4: Quality Impact Assessment
  - Materials: Medium risk (supplier changes)
  - Labor: Low risk (automation improves consistency)
  - Overhead: Low risk (no direct quality impact)

  STEP 4: Synthesis
  Total potential savings: $200K (meets target)
  Recommended approach:
  1. Start with overhead (lowest risk): $40K
  2. Implement labor efficiencies: $60K
  3. Negotiate materials carefully: $100K

  STEP 5: Verification
  ✓ Math checks out: $200K = 20% of $1M
  ✓ Strategies are actionable
  ✓ Quality risks identified and manageable

  Final Answer:
  The company can achieve 20% cost reduction through a phased approach:
  - Phase 1 (Low risk): Overhead optimization ($40K)
  - Phase 2 (Low-medium risk): Labor efficiency ($60K)
  - Phase 3 (Medium risk): Materials optimization ($100K)

  This strategy minimizes quality risk while achieving the target.

  Confidence: HIGH
  ```
</Accordion>

***

### Task 2: Knowledge-Grounded QA System (30 points)

Build a RAG-based system that answers questions using provided knowledge.

**Requirements:**

* Retrieve and use relevant context
* Cite sources explicitly
* Admit when information is insufficient
* Prevent hallucinations

<Accordion title="System Prompt Template">
  ```
  You are a knowledge-grounded assistant. Answer questions using ONLY the provided context.

  Knowledge Base:
  [RETRIEVED DOCUMENTS]

  Question: [USER QUERY]

  Grounding Rules:
  1. Answer ONLY from provided context
  2. Cite sources: [Doc X]
  3. If information missing: State explicitly
  4. Never infer or assume
  5. Provide confidence level

  Response Format:
  Answer: [GROUNDED RESPONSE]

  Sources: [LIST OF DOCS USED]

  Confidence: [HIGH/MEDIUM/LOW]

  Limitations: [IF ANY]
  ```
</Accordion>

**Test Case:**
Provide 3 documents about a fictional product and answer questions about it.

<Accordion title="Sample Implementation">
  ```
  Knowledge Base:

  [Doc 1 - Product Specs]:
  "The XR-2000 smartwatch features a 1.4-inch AMOLED display, 7-day battery life, 
  and water resistance up to 50 meters. It includes heart rate monitoring, GPS tracking, 
  and sleep analysis. Price: $299."

  [Doc 2 - Customer Reviews]:
  "Average rating: 4.5/5 stars from 1,247 reviews. Users praise the battery life 
  and display quality. Common complaints include limited app selection and occasional 
  GPS accuracy issues."

  [Doc 3 - Warranty]:
  "1-year manufacturer warranty covers defects. Extended 2-year warranty available 
  for $49. Warranty does not cover water damage, physical damage, or battery degradation."

  Question: "Is the XR-2000 good for swimming and what's the warranty?"

  Response:

  Answer: Yes, the XR-2000 is suitable for swimming as it has water resistance up 
  to 50 meters [Doc 1]. However, it's important to note that the warranty does NOT 
  cover water damage [Doc 3], so while the device is designed for water use, any 
  water-related issues would not be covered under warranty.

  The watch comes with a 1-year manufacturer warranty that covers defects, with an 
  optional 2-year extended warranty available for $49 [Doc 3].

  Sources:
  - [Doc 1]: Product specifications (water resistance)
  - [Doc 3]: Warranty terms

  Confidence: HIGH (information directly stated in sources)

  Limitations: The documents don't specify what "water resistance up to 50 meters" 
  means in practical terms (e.g., suitable for swimming vs. diving), though 50 meters 
  typically indicates swimming is safe.
  ```
</Accordion>

***

### Task 3: Tool-Integrated Assistant (40 points)

Build a system that uses external tools to accomplish tasks.

**Requirements:**

* Identify when tools are needed
* Execute tools with proper parameters
* Handle tool errors gracefully
* Combine tool outputs with reasoning

<Accordion title="System Prompt Template">
  ```
  You are an assistant with access to external tools.

  Available Tools:
  1. calculator(expression) - Evaluates math expressions
  2. search(query) - Searches knowledge base
  3. get_data(source, parameters) - Retrieves data from sources

  User Request: [QUERY]

  Process:

  STEP 1: Analyze Request
  What information/actions are needed?
  Which tools are required?

  STEP 2: Plan Tool Usage
  Tool sequence:
  1. [TOOL] with [PARAMETERS]
  2. [TOOL] with [PARAMETERS]

  STEP 3: Execute Tools
  Tool: [NAME]
  Input: [PARAMETERS]
  Output: [RESULT]

  [Repeat for each tool]

  STEP 4: Synthesize Response
  Using tool outputs:
  [COMPREHENSIVE ANSWER]

  STEP 5: Verify
  ✓ All tools executed successfully?
  ✓ Results make sense?
  ✓ Answer is complete?

  Final Response: [ANSWER]
  ```
</Accordion>

**Test Case:**
"Calculate the compound interest on \$10,000 invested at 6% annually for 5 years, then compare it to simple interest for the same period."

<Accordion title="Expected Output">
  ```
  STEP 1: Analyze Request
  Need to:
  - Calculate compound interest
  - Calculate simple interest
  - Compare the two

  Tools needed: calculator

  STEP 2: Plan Tool Usage
  1. Calculate compound interest: A = P(1 + r)^t
  2. Calculate simple interest: I = Prt
  3. Calculate difference

  STEP 3: Execute Tools

  Tool: calculator
  Input: "10000 * (1 + 0.06)^5"
  Output: 13382.26

  Tool: calculator
  Input: "10000 * 0.06 * 5"
  Output: 3000.00

  Tool: calculator
  Input: "13382.26 - (10000 + 3000)"
  Output: 382.26

  STEP 4: Synthesize Response

  Compound Interest:
  - Formula: A = P(1 + r)^t
  - Calculation: $10,000 × (1.06)^5 = $13,382.26
  - Interest earned: $3,382.26

  Simple Interest:
  - Formula: I = Prt
  - Calculation: $10,000 × 0.06 × 5 = $3,000
  - Total amount: $13,000

  Comparison:
  - Compound interest earns $382.26 more than simple interest
  - This represents a 12.7% higher return
  - The difference comes from "interest on interest" in compound calculation

  STEP 5: Verify
  ✓ Compound interest formula correct
  ✓ Simple interest formula correct
  ✓ Comparison calculation accurate

  Final Response:
  Over 5 years, compound interest ($3,382.26) earns $382.26 more than simple 
  interest ($3,000) on a $10,000 investment at 6% annual rate. Your final amount 
  would be $13,382.26 with compound interest versus $13,000 with simple interest.
  ```
</Accordion>

***

## Scoring Rubric

### Multiple Choice (50 points)

* 2 points per question
* 20/25 correct required to pass (40/50 points)

### Final Project (50 points)

**Task 1: Complex Reasoning (15 points)**

* Proper use of CoT: 5 points
* Effective decomposition: 5 points
* Verification and confidence: 5 points

**Task 2: Knowledge-Grounded QA (15 points)**

* Strict grounding (no hallucinations): 5 points
* Proper source citation: 5 points
* Handling missing information: 5 points

**Task 3: Tool Integration (20 points)**

* Correct tool identification: 5 points
* Proper tool execution: 5 points
* Error handling: 5 points
* Result synthesis: 5 points

**Total: 100 points**\
**Passing Score: 80 points**

***

## Evaluation Criteria

<CardGroup cols={2}>
  <Card title="Technical Accuracy" icon="bullseye">
    Correct application of techniques and accurate results
  </Card>

  <Card title="Integration" icon="puzzle-piece">
    Effective combination of multiple techniques
  </Card>

  <Card title="Robustness" icon="shield">
    Handles edge cases and errors gracefully
  </Card>

  <Card title="Clarity" icon="lightbulb">
    Clear reasoning and well-structured outputs
  </Card>
</CardGroup>

***

## Course Completion

<Check>Complete all 25 multiple-choice questions</Check>
<Check>Implement Task 1: Complex Reasoning System</Check>
<Check>Implement Task 2: Knowledge-Grounded QA</Check>
<Check>Implement Task 3: Tool-Integrated Assistant</Check>
<Check>Test your system with provided test cases</Check>
<Check>Verify all components work together</Check>

## Congratulations!

Upon completing this assessment, you will have demonstrated mastery of:

* ✅ Foundational prompting principles (Module 1)
* ✅ Task-specific prompting patterns (Module 2)
* ✅ Advanced prompting techniques (Module 3)

You're now equipped to build sophisticated AI systems using state-of-the-art prompting techniques!

<Card title="View Course Certificate" icon="award" href="/certificate">
  Claim your Prompt University completion certificate
</Card>