AI Algorithm Evaluation: Lessons Learned
Pancake Sorting Challenge Follow-Up
This lecture debriefs the pancake sorting challenge and discusses the key insights about working with AI for algorithmic problem solving.
Activity Debrief
Your Experience with AI and Algorithms
What did we discover together?
Let’s reflect on your pancake sorting adventure and extract the key insights about working with AI for algorithmic problem solving.
Common Challenges Faced
Based on your submissions, typical struggles included:
- AI generated standard sorting instead of flip operations
- Missing edge case handling for empty arrays
- Incorrect output format (just array vs. full result object)
- Flip operations that violated the “from index 0” constraint
- Overly complex solutions for simple cases
- Incomplete or missing flip sequence tracking
Sound familiar?
The Good News
What worked well across teams (observations):
- Groups with detailed prompts got significantly better results
- TRACE evaluation helped identify issues systematically
- Testing revealed problems that weren’t obvious from code reading
- Most teams could improve AI output through iteration
- Reflection led to genuine insights about AI capabilities
Prompt Evolution
The Journey
Let’s examine a real example of prompt evolution that demonstrates the power of iterative improvement.
Our scenario: Getting AI to implement proper pancake sorting
Iteration 1: The Basic Attempt
Student’s first prompt:
Write a pancake sort algorithm in Python.
AI Response:
Problems with Iteration 1
TRACE Analysis revealed multiple issues:
Test Results:
- Basic functionality: FAILED (wrong return format)
- Duplicates: FAILED (logic errors)
- Edge cases: FAILED (crashes on empty array)
- Flip validation: FAILED (no operation tracking)
Iteration 2: Adding Context and Requirements
Improved prompt:
I need a pancake sorting algorithm for a robotic pancake flipper.
The robot can only flip pancakes from the top of the stack down to
any position. The function should:
- Take a list of pancake sizes
- Only use flip operations from index 0 to position k
- Return a dictionary with sorted array, flip sequence, and total flips
- Handle edge cases like empty arrays
- Work with duplicate values
Include the flip operation as a helper function.
Iteration 2 Results
AI Response (excerpt):
def flip(arr, k):
return arr[:k+1][::-1] + arr[k+1:]
def pancake_sort(pancakes):
if len(pancakes) <= 1:
return {
'sorted_array': pancakes,
'flip_sequence': [],
'total_flips': 0
}
arr = pancakes.copy()
flips = []
for i in range(len(arr), 1, -1):
max_idx = arr.index(max(arr[:i]))
if max_idx != 0:
arr = flip(arr, max_idx)
flips.append(max_idx)
if arr[0] != arr[i-1]:
arr = flip(arr, i-1)
flips.append(i-1)
return {
'sorted_array': arr,
'flip_sequence': flips,
'total_flips': len(flips)
}
Progress in Iteration 2
Major improvements:
- Proper output format with required keys
- Dedicated flip helper function
- Basic edge case handling
- Flip sequence tracking
- Clear algorithm structure
Remaining issues:
- Still fails some duplicate tests
- Missing detailed validation
- No operations log for debugging
- Algorithm could be more efficient
Iteration 3: The Refined Solution
Final optimized prompt:
Create a comprehensive pancake sorting implementation for a robotic
breakfast system with these exact specifications:
CONTEXT: Robot arm can only flip pancakes from position 0 down to any
position k. This reverses elements from index 0 to k inclusive.
REQUIREMENTS:
- Function signature: pancake_sort(pancakes, options=None)
- Input: list of comparable elements (integers representing sizes)
- Output: dictionary with keys: 'sorted_array', 'flip_sequence',
'total_flips', 'is_sorted', 'operations_log'
CONSTRAINTS:
- Only flip operations allowed (no swaps, inserts, etc.)
- All flips must start from index 0
- Must preserve all original elements
- Handle duplicates correctly
EDGE CASES:
- Empty array: return empty array, no flips
- Single element: return unchanged, no flips
- Already sorted: minimize flips needed
- All identical: handle efficiently
EXAMPLE:
Input: [3, 1, 2]
Process: Find max (3) at 0, flip to end → [2, 1, 3]
Find max (2) at 0, flip to position → [1, 2, 3]
Output: {'sorted_array': [1, 2, 3], 'flip_sequence': [2, 1],
'total_flips': 2, 'is_sorted': True}
Include comprehensive error checking and detailed comments
explaining the pancake sorting strategy.
Iteration 3 Results
AI delivered a robust solution:
def pancake_sort(pancakes, options=None):
"""
Pancake sorting algorithm - sorts by flipping from index 0 only.
Args:
pancakes: List of comparable elements
options: Optional dict for algorithm parameters
Returns:
Dict with sorted_array, flip_sequence, total_flips,
is_sorted, operations_log
"""
# Input validation
if not isinstance(pancakes, list):
raise TypeError("Input must be a list")
if len(pancakes) <= 1:
return {
'sorted_array': pancakes.copy(),
'flip_sequence': [],
'total_flips': 0,
'is_sorted': True,
'operations_log': []
}
arr = pancakes.copy()
flips = []
operations = []
def flip(array, k):
"""Flip elements from index 0 to k inclusive"""
return array[:k+1][::-1] + array[k+1:]
# Main pancake sorting algorithm
n = len(arr)
for size in range(n, 1, -1):
# Find position of largest unsorted element
max_idx = 0
for i in range(size):
if arr[i] > arr[max_idx]:
max_idx = i
# If largest element is not at front, flip it to front
if max_idx != 0:
arr = flip(arr, max_idx)
flips.append(max_idx)
operations.append((max_idx, arr.copy()))
# If largest element is not at correct position, flip to position
if max_idx != size - 1 or arr[0] > arr[size - 1]:
arr = flip(arr, size - 1)
flips.append(size - 1)
operations.append((size - 1, arr.copy()))
return {
'sorted_array': arr,
'flip_sequence': flips,
'total_flips': len(flips),
'is_sorted': all(arr[i] <= arr[i+1] for i in range(len(arr)-1)),
'operations_log': operations
}
Test Results: Iteration 3
Comprehensive success:
- Basic functionality: PASSED ✓
- Duplicates: PASSED ✓
- Reverse sorted: PASSED ✓
- Edge cases: PASSED ✓
- Flip validation: PASSED ✓
- Identical elements: PASSED ✓
Performance: Efficient O(n²) implementation with proper flip tracking
Key Improvements Between Iterations
Iteration 1 → 2: Adding Context
What changed:
Impact:
- AI understood the domain (robotic pancake flipper)
- Generated appropriate constraints (flip from index 0)
- Included required output format
- Added basic edge case handling
Iteration 2 → 3: Comprehensive Specification
Major enhancements:
- Detailed examples with step-by-step walkthrough
- Explicit edge cases with expected behaviors
- Precise input/output specifications
- Algorithm explanation requirements
- Error handling requirements
- Performance considerations
The difference: Moving from “what” to “exactly how and why”
The Transformation Pattern
Common progression we observed:
- Basic request → Generic, often wrong solution
- Context added → Domain-appropriate but incomplete
- Specifications detailed → Robust, testable solution
Key insight: Specificity and context are multiplicative, not additive
Critical Insights
TRACE Method Effectiveness
What your submissions are revealing:
The TRACE method caught issues that manual inspection missed
Evident AI Strengths
Where AI excelled:
- Pattern recognition in algorithmic structures
- Code generation speed for well-specified problems
- Syntax correctness and basic error handling
- Documentation when specifically requested
- Multiple approaches when asked for alternatives
Evident AI Limitations
Consistent weak points:
- Algorithm selection without clear guidance
- Edge case imagination - needs explicit examples
- Optimization decisions without performance requirements
- Testing strategy - rarely suggests comprehensive tests
- Domain knowledge assumptions can be wrong
The Prompt Engineering Takeaways
What separates effective from ineffective prompts:
- Specific examples with expected outputs
- Constraint explanation with reasoning
- Edge case enumeration with handling requirements
- Output format specification with all required fields
- Algorithm strategy hints when appropriate
It’s not just being detailed - it’s being strategically detailed
Broader Implications for Coding
The New Coding Workflow
Traditional approach:
AI-Assisted approach:
Key difference: Prompt design becomes as important as algorithm design
Skills That Matter More Now
Rising in importance:
- Specification writing - precise requirement articulation
- Evaluation frameworks - systematic quality assessment
- Testing strategy - comprehensive validation approaches
- Prompt engineering - effective AI communication
- Critical analysis - distinguishing good from adequate code
Still essential but changing:
- Algorithm knowledge - for evaluation and debugging
- Code reading - for AI output assessment
- System design - for integration planning
Quality Assurance Evolution
New responsibilities:
- AI output validation replaces some manual testing
- Prompt version control for reproducible results
- Evaluation criteria definition for consistent quality
- AI model selection for different problem types
- Bias detection in AI-generated solutions
Takeaways for Future Projects
The Prompt Engineering Playbook
For algorithmic problems, always include:
- Domain context - what real problem this solves
- Operation constraints - what’s allowed and forbidden
- Input/output examples - concrete illustrations
- Edge cases - boundary conditions and expected handling
- Performance expectations - time/space complexity needs
- Output format - exact structure required
- Validation requirements - how success is measured
The Evaluation Checklist
Before trusting any AI-generated algorithm:
- Apply systematic evaluation (TRACE or similar)
- Run comprehensive test suite including edge cases
- Verify algorithmic complexity claims
- Check for constraint violations
- Validate output format completeness
- Assess code maintainability and documentation
When to Use AI for Algorithms
Good candidates:
- Well-understood problem domains
- Standard algorithmic patterns with twists
- Implementation of known algorithms
- Code translation between languages
- Performance optimization of existing code
Proceed with caution:
- Novel algorithmic research
- Critical performance requirements
- Complex constraint satisfaction
- Domain-specific optimization
- Security-sensitive implementations
Building on the Relationship with Generative AI
Practice these techniques:
- Iterative refinement - start broad, narrow systematically
- Specification decomposition - break complex requirements down
- Example-driven explanation - show don’t just tell
- Constraint articulation - be explicit about limitations
- Quality measurement - define success criteria upfront