"""
Pattern 060: Curriculum Learning
Description:
Curriculum Learning structures the learning process by presenting training
examples in a meaningful order, from simple to complex. Like human education,
this progressive approach helps agents build foundational skills before
tackling harder problems, leading to faster learning and better performance.
Components:
1. Difficulty Scorer: Evaluates task complexity
2. Curriculum Designer: Orders tasks by difficulty
3. Pacing Strategy: Determines progression speed
4. Performance Monitor: Tracks learning progress
5. Adaptive Scheduler: Adjusts curriculum based on performance
Use Cases:
- Training complex reasoning agents
- Gradual skill acquisition
- Hierarchical task learning
- Educational tutoring systems
- Reinforcement learning with sparse rewards
- Knowledge scaffolding
LangChain Implementation:
Implements curriculum learning by ordering prompts and examples from
simple to complex, adapting progression based on agent performance.
"""
import os
import time
from typing import Dict, Any, List, Optional, Tuple, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import statistics
import random
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
load_dotenv()
class DifficultyLevel(Enum):
"""Difficulty levels for curriculum"""
TRIVIAL = 1
EASY = 2
MEDIUM = 3
HARD = 4
EXPERT = 5
class PacingStrategy(Enum):
"""Strategies for curriculum pacing"""
FIXED = "fixed" # Predetermined progression
ADAPTIVE = "adaptive" # Based on performance
MIXED = "mixed" # Combine easy and hard
SPIRAL = "spiral" # Revisit with increasing complexity
COMPETENCY_BASED = "competency_based" # Advance when mastered
@dataclass
class LearningExample:
"""A single learning example"""
example_id: str
task_type: str
input_text: str
expected_output: str
difficulty: DifficultyLevel
prerequisites: List[str] = field(default_factory=list)
concepts: List[str] = field(default_factory=list)
metadata: Dict[str, Any] = field(default_factory=dict)
def is_prerequisite_met(self, mastered_examples: Set[str]) -> bool:
"""Check if prerequisites are satisfied"""
return all(prereq in mastered_examples for prereq in self.prerequisites)
@dataclass
class CurriculumStage:
"""A stage in the curriculum"""
stage_id: int
stage_name: str
difficulty_range: Tuple[DifficultyLevel, DifficultyLevel]
examples: List[LearningExample]
mastery_threshold: float = 0.8
@property
def num_examples(self) -> int:
return len(self.examples)
@dataclass
class PerformanceRecord:
"""Record of performance on an example"""
example_id: str
attempt_number: int
correct: bool
response: str
timestamp: datetime
time_taken_ms: float
@dataclass
class CurriculumResult:
"""Result from curriculum learning"""
stages_completed: int
total_examples: int
examples_mastered: int
overall_accuracy: float
learning_curve: List[Tuple[int, float]] # (example_num, accuracy)
time_per_stage: List[float]
final_performance: float
total_time_ms: float
def to_dict(self) -> Dict[str, Any]:
return {
"stages_completed": self.stages_completed,
"examples_mastered": f"{self.examples_mastered}/{self.total_examples}",
"overall_accuracy": f"{self.overall_accuracy:.1%}",
"final_performance": f"{self.final_performance:.1%}",
"total_time_ms": f"{self.total_time_ms:.1f}"
}
class CurriculumLearner:
"""
Agent that learns through curriculum progression.
Features:
1. Difficulty-ordered example presentation
2. Performance-based pacing
3. Mastery tracking
4. Adaptive progression
5. Skill scaffolding
"""
def __init__(
self,
pacing_strategy: PacingStrategy = PacingStrategy.ADAPTIVE,
mastery_threshold: float = 0.8,
temperature: float = 0.7
):
self.pacing_strategy = pacing_strategy
self.mastery_threshold = mastery_threshold
self.temperature = temperature
# Learner LLM
self.learner = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=temperature
)
# Difficulty assessor
self.assessor = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0.2
)
# Learning state
self.mastered_examples: Set[str] = set()
self.performance_history: List[PerformanceRecord] = []
self.current_stage: int = 0
# Learning statistics
self.learning_curve: List[Tuple[int, float]] = []
def _assess_difficulty(self, example: LearningExample) -> DifficultyLevel:
"""Assess difficulty of an example"""
# If already labeled, use that
if example.difficulty != DifficultyLevel.MEDIUM:
return example.difficulty
# Otherwise, assess automatically
assessment_prompt = ChatPromptTemplate.from_messages([
("system", """Rate the difficulty of this learning task on a scale:
1 - Trivial (very simple, obvious answer)
2 - Easy (straightforward, basic reasoning)
3 - Medium (requires some thought)
4 - Hard (complex, multiple steps)
5 - Expert (very challenging, advanced concepts)
Respond with just the number."""),
("user", "Task: {input}\nExpected: {output}\n\nDifficulty:")
])
chain = assessment_prompt | self.assessor | StrOutputParser()
try:
rating = chain.invoke({
"input": example.input_text,
"output": example.expected_output
})
difficulty_num = int(''.join(c for c in rating if c.isdigit()))
return DifficultyLevel(min(max(difficulty_num, 1), 5))
except:
return DifficultyLevel.MEDIUM
def _create_curriculum(
self,
examples: List[LearningExample]
) -> List[CurriculumStage]:
"""Create curriculum stages from examples"""
# Assess difficulties if needed
for example in examples:
if example.difficulty == DifficultyLevel.MEDIUM and not example.metadata.get("assessed"):
example.difficulty = self._assess_difficulty(example)
example.metadata["assessed"] = True
# Group by difficulty
stages_dict: Dict[DifficultyLevel, List[LearningExample]] = {}
for example in examples:
if example.difficulty not in stages_dict:
stages_dict[example.difficulty] = []
stages_dict[example.difficulty].append(example)
# Create stages
stages = []
for difficulty in sorted(stages_dict.keys(), key=lambda x: x.value):
stage = CurriculumStage(
stage_id=len(stages),
stage_name=f"Stage {len(stages)+1}: {difficulty.name}",
difficulty_range=(difficulty, difficulty),
examples=stages_dict[difficulty],
mastery_threshold=self.mastery_threshold
)
stages.append(stage)
return stages
def _attempt_example(
self,
example: LearningExample,
previous_examples: List[LearningExample]
) -> PerformanceRecord:
"""Attempt to solve an example"""
start_time = time.time()
# Build context from previous examples
context = ""
if previous_examples:
context = "Previous examples:\n" + "\n".join([
f"Input: {ex.input_text}\nOutput: {ex.expected_output}"
for ex in previous_examples[-3:] # Last 3 examples
]) + "\n\n"
# Generate response
prompt = ChatPromptTemplate.from_messages([
("system", """You are learning to solve tasks step by step.
{context}Now solve this task:"""),
("user", "{input}")
])
chain = prompt | self.learner | StrOutputParser()
response = chain.invoke({
"context": context,
"input": example.input_text
})
time_taken_ms = (time.time() - start_time) * 1000
# Check correctness (simplified)
response_normalized = response.strip().lower()
expected_normalized = example.expected_output.strip().lower()
# Flexible matching
correct = (
response_normalized == expected_normalized or
expected_normalized in response_normalized or
response_normalized in expected_normalized
)
# Count attempts
attempt_number = len([
r for r in self.performance_history
if r.example_id == example.example_id
]) + 1
record = PerformanceRecord(
example_id=example.example_id,
attempt_number=attempt_number,
correct=correct,
response=response,
timestamp=datetime.now(),
time_taken_ms=time_taken_ms
)
self.performance_history.append(record)
return record
def _calculate_stage_performance(
self,
stage: CurriculumStage
) -> float:
"""Calculate performance on current stage"""
stage_example_ids = {ex.example_id for ex in stage.examples}
recent_records = [
r for r in self.performance_history[-20:] # Last 20 attempts
if r.example_id in stage_example_ids
]
if not recent_records:
return 0.0
correct_count = sum(1 for r in recent_records if r.correct)
return correct_count / len(recent_records)
def _should_advance(
self,
stage: CurriculumStage,
attempts_on_stage: int
) -> bool:
"""Determine if should advance to next stage"""
if self.pacing_strategy == PacingStrategy.FIXED:
# Fixed progression after N attempts
return attempts_on_stage >= len(stage.examples) * 2
elif self.pacing_strategy == PacingStrategy.ADAPTIVE:
# Advance when performance exceeds threshold
performance = self._calculate_stage_performance(stage)
return performance >= stage.mastery_threshold and attempts_on_stage >= len(stage.examples)
elif self.pacing_strategy == PacingStrategy.COMPETENCY_BASED:
# Advance when all examples mastered
stage_example_ids = {ex.example_id for ex in stage.examples}
mastered_in_stage = stage_example_ids & self.mastered_examples
return len(mastered_in_stage) >= len(stage_example_ids) * stage.mastery_threshold
else:
# Default: adaptive
performance = self._calculate_stage_performance(stage)
return performance >= stage.mastery_threshold
def learn_curriculum(
self,
examples: List[LearningExample],
max_attempts_per_stage: int = 20
) -> CurriculumResult:
"""Learn through curriculum progression"""
start_time = time.time()
print(f"\nš Starting curriculum learning with {len(examples)} examples...")
# Create curriculum
stages = self._create_curriculum(examples)
print(f"\nšÆ Curriculum Structure:")
for stage in stages:
print(f" {stage.stage_name}: {stage.num_examples} examples")
# Learn through stages
time_per_stage = []
for stage_idx, stage in enumerate(stages):
stage_start = time.time()
print(f"\nš Stage {stage_idx + 1}/{len(stages)}: {stage.stage_name}")
print(f" Examples: {stage.num_examples}")
print(f" Mastery Threshold: {stage.mastery_threshold:.0%}")
attempts_on_stage = 0
previous_examples = []
# Learn examples in this stage
while attempts_on_stage float:
"""Calculate accuracy over recent attempts"""
if not self.performance_history:
return 0.0
recent = self.performance_history[-window:]
correct = sum(1 for r in recent if r.correct)
return correct / len(recent)
def demonstrate_curriculum_learning():
"""Demonstrate Curriculum Learning pattern"""
print("=" * 80)
print("PATTERN 060: CURRICULUM LEARNING DEMONSTRATION")
print("=" * 80)
print("\nProgressive learning from simple to complex\n")
# Create learning examples with varying difficulty
examples = [
# Trivial examples
LearningExample("ex1", "math", "What is 2 + 2?", "4", DifficultyLevel.TRIVIAL),
LearningExample("ex2", "math", "What is 5 + 3?", "8", DifficultyLevel.TRIVIAL),
LearningExample("ex3", "math", "What is 10 - 6?", "4", DifficultyLevel.TRIVIAL),
# Easy examples
LearningExample("ex4", "math", "What is 12 + 15?", "27", DifficultyLevel.EASY, prerequisites=["ex1"]),
LearningExample("ex5", "math", "What is 25 - 8?", "17", DifficultyLevel.EASY, prerequisites=["ex3"]),
LearningExample("ex6", "math", "What is 7 times 3?", "21", DifficultyLevel.EASY),
# Medium examples
LearningExample("ex7", "math", "What is 15 times 4?", "60", DifficultyLevel.MEDIUM, prerequisites=["ex6"]),
LearningExample("ex8", "math", "What is 100 divided by 4?", "25", DifficultyLevel.MEDIUM),
LearningExample("ex9", "math", "What is (5 + 3) times 2?", "16", DifficultyLevel.MEDIUM, prerequisites=["ex4", "ex6"]),
# Hard examples
LearningExample("ex10", "math", "What is 25% of 80?", "20", DifficultyLevel.HARD, prerequisites=["ex8"]),
LearningExample("ex11", "math", "If x + 5 = 12, what is x?", "7", DifficultyLevel.HARD, prerequisites=["ex4"]),
]
# Test 1: Adaptive curriculum
print("\n" + "=" * 80)
print("TEST 1: Adaptive Curriculum Learning")
print("=" * 80)
learner1 = CurriculumLearner(
pacing_strategy=PacingStrategy.ADAPTIVE,
mastery_threshold=0.7
)
result1 = learner1.learn_curriculum(examples, max_attempts_per_stage=15)
print(f"\nš Learning Results:")
print(f" Stages Completed: {result1.stages_completed}")
print(f" Examples Mastered: {result1.examples_mastered}/{result1.total_examples}")
print(f" Overall Accuracy: {result1.overall_accuracy:.1%}")
print(f" Final Performance: {result1.final_performance:.1%}")
print(f" Total Time: {result1.total_time_ms/1000:.1f}s")
print(f"\nš Learning Curve (samples):")
for i in range(0, len(result1.learning_curve), max(1, len(result1.learning_curve)//5)):
attempt_num, accuracy = result1.learning_curve[i]
print(f" Attempt {attempt_num}: {accuracy:.1%} accuracy")
print(f"\nā±ļø Time per Stage:")
for i, stage_time in enumerate(result1.time_per_stage, 1):
print(f" Stage {i}: {stage_time:.1f}s")
# Test 2: Fixed pacing
print("\n" + "=" * 80)
print("TEST 2: Fixed Pacing Curriculum")
print("=" * 80)
learner2 = CurriculumLearner(
pacing_strategy=PacingStrategy.FIXED,
mastery_threshold=0.6
)
# Use subset for faster demo
subset_examples = examples[:8]
result2 = learner2.learn_curriculum(subset_examples, max_attempts_per_stage=10)
print(f"\nš Fixed Pacing Results:")
print(f" Stages: {result2.stages_completed}")
print(f" Mastered: {result2.examples_mastered}/{result2.total_examples}")
print(f" Accuracy: {result2.overall_accuracy:.1%}")
# Test 3: Learning curve comparison
print("\n" + "=" * 80)
print("TEST 3: Learning Progress Visualization")
print("=" * 80)
print(f"\nš Performance Improvement:")
curve = result1.learning_curve
# Show improvement
if len(curve) >= 3:
early_perf = statistics.mean([acc for _, acc in curve[:3]])
mid_perf = statistics.mean([acc for _, acc in curve[len(curve)//2:len(curve)//2+3]])
late_perf = statistics.mean([acc for _, acc in curve[-3:]])
print(f" Early (first 3): {early_perf:.1%}")
print(f" Middle: {mid_perf:.1%}")
print(f" Late (last 3): {late_perf:.1%}")
print(f" Improvement: +{(late_perf - early_perf):.1%}")
# Summary
print("\n" + "=" * 80)
print("CURRICULUM LEARNING PATTERN SUMMARY")
print("=" * 80)
print("""
Key Benefits:
1. Faster Learning: Build on foundations progressively
2. Better Performance: Master basics before advanced topics
3. Reduced Frustration: Appropriate difficulty at each stage
4. Stable Training: Avoid overwhelming with complexity
5. Improved Retention: Scaffold knowledge systematically
Curriculum Design:
1. Difficulty Ordering: Simple ā Complex
2. Prerequisite Structure: Build dependencies
3. Concept Progression: Foundational ā Advanced
4. Skill Scaffolding: Support skill development
5. Spiral Learning: Revisit concepts with depth
Difficulty Assessment:
- Automatic: LLM-based complexity scoring
- Manual: Expert labeling
- Hybrid: Combine both approaches
- Dynamic: Adjust based on learner performance
- Multi-dimensional: Consider multiple factors
Pacing Strategies:
1. Fixed: Predetermined progression
- Simple, predictable
- May not fit all learners
2. Adaptive: Performance-based
- Personalized pace
- More complex logic
3. Mixed: Interleave difficulties
- Maintain engagement
- Prevent boredom
4. Spiral: Revisit with complexity
- Reinforce learning
- Deepen understanding
5. Competency-Based: Master before advance
- Ensure readiness
- May be slower
Curriculum Stages:
1. Foundation: Basic concepts
2. Development: Core skills
3. Integration: Combine concepts
4. Application: Real problems
5. Mastery: Advanced challenges
Performance Monitoring:
- Accuracy tracking
- Time per example
- Mastery identification
- Learning curve analysis
- Plateau detection
Advancement Criteria:
- Accuracy threshold (e.g., 80%)
- Minimum attempts per stage
- Mastery of N examples
- Time-based progression
- Adaptive combinations
Use Cases:
- Educational tutoring systems
- Agent skill development
- Reinforcement learning
- Language learning
- Technical training
- Complex task acquisition
Best Practices:
1. Clear difficulty progression
2. Prerequisite tracking
3. Performance monitoring
4. Adaptive pacing
5. Regular assessment
6. Sufficient practice
7. Gradual complexity increase
Production Considerations:
- Curriculum versioning
- Learner state persistence
- Performance analytics
- A/B testing curricula
- Difficulty calibration
- Prerequisite validation
- Scalability
Comparison with Related Patterns:
- vs. Random: Structured vs unordered
- vs. Hard Example Mining: Systematic vs selection
- vs. Meta-Learning: Progression vs strategy learning
- vs. Active Learning: Curriculum vs query strategy
Challenges:
1. Difficulty assessment accuracy
2. Optimal ordering
3. Individual differences
4. Curriculum generalization
5. Pacing calibration
Research Findings:
- 30-50% faster learning vs random
- Better final performance
- Improved generalization
- Reduced training instability
- Context-dependent effectiveness
The Curriculum Learning pattern enables efficient learning through
strategic ordering of examples from simple to complex, mirroring
effective human educational approaches.
""")
if __name__ == "__main__":
demonstrate_curriculum_learning()
Agentic Design Pattern: Curriculum Learning
Pattern 060: Curriculum Learning – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Curriculum Learning pattern implementation.
Pattern Description
Pattern 060: Curriculum Learning
Description: Curriculum Learning structures the learning process by presenting training examples in a meaningful order, from simple to complex. Like human education, this progressive approach helps agents build foundational skills before tackling harder problems, leading to faster learning and better performance.
Components: 1. Difficulty Scorer: Evaluates task complexity 2. Curriculum Designer: Orders tasks by difficulty 3. Pacing Strategy: Determines progression speed 4. Performance Monitor: Tracks learning progress 5. Adaptive Scheduler: Adjusts curriculum based on performance
Use Cases: – Training complex reasoning agents – Gradual skill acquisition – Hierarchical task learning – Educational tutoring systems – Reinforcement learning with sparse rewards – Knowledge scaffolding
LangChain Implementation: Implements curriculum learning by ordering prompts and examples from simple to complex, adapting progression based on agent performance.
File Statistics
- Total Lines: 666
- Classes: 7
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 060: Curriculum Learning
Description:
Curriculum Learning structures the learning process by presenting training
examples in a meaningful order, from simple to complex. Like human education,
this progressive approach helps agents build foundational skills before
tackling harder problems, leading to faster learning and better performance.
Components:
1. Difficulty Scorer: Evaluates task complexity
2. Curriculum Designer: Orders tasks by difficulty
3. Pacing Strategy: Determines progression speed
4. Performance Monitor: Tracks learning progress
5. Adaptive Scheduler: Adjusts curriculum based on performance
Use Cases:
- Training complex reasoning agents
- Gradual skill acquisition
- Hierarchical task learning
- Educational tutoring systems
- Reinforcement learning with sparse rewards
- Knowledge scaffolding
LangChain Implementation:
Implements curriculum learning by ordering prompts and examples from
simple to complex, adapting progression based on agent performance.
"""
Purpose: This module implements the Curriculum Learning pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import random
import time
import typing.Any
import typing.Callable
import typing.Dict
import typing.List
import typing.Optional
import typing.Tuple
Purpose: Core Python functionality:
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– datetime.datetime: Date and time handling
– enum.Enum: Enumeration support
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– random: Random number generation
– time: Time-related functions
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Callable: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Tuple: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from dotenv.load_dotenv
from statistics
Purpose: Additional dependencies for specialized functionality.
Classes
Class: DifficultyLevel
Lines 47-53
Inherits from: Enum
Purpose:
Difficulty levels for curriculum
Class: PacingStrategy
Lines 56-62
Inherits from: Enum
Purpose:
Strategies for curriculum pacing
Class: LearningExample
Lines 66-79
Purpose:
A single learning example
Methods:
is_prerequisite_met(self, mastered_examples)
Lines 77-79
Purpose:
Check if prerequisites are satisfied
Parameters:
– mastered_examples: Mastered examples
Returns: bool
Class: CurriculumStage
Lines 83-93
Purpose:
A stage in the curriculum
Methods:
num_examples(self)
Lines 92-93
Decorators: property
Purpose: Num Examples operation.
Parameters:
Returns: int
Class: PerformanceRecord
Lines 97-104
Purpose:
Record of performance on an example
Class: CurriculumResult
Lines 108-126
Purpose:
Result from curriculum learning
Methods:
to_dict(self)
Lines 119-126
Purpose: To Dict operation.
Parameters:
Returns: <ast.Subscript object at 0x000001A06D700050>
Class: CurriculumLearner
Lines 129-444
Purpose:
Agent that learns through curriculum progression.
Features:
1. Difficulty-ordered example presentation
2. Performance-based pacing
3. Mastery tracking
4. Adaptive progression
5. Skill scaffolding
Methods:
__init__(self, pacing_strategy, mastery_threshold, temperature)
Lines 141-169
Purpose: Initialize the class instance with required parameters.
Parameters:
– pacing_strategy: Pacing strategy
– mastery_threshold: Mastery threshold
– temperature: Sampling temperature (0-1)
_assess_difficulty(self, example)
Lines 171-202
Purpose:
Assess difficulty of an example
Parameters:
– example: Example
Returns: DifficultyLevel
_create_curriculum(self, examples)
Lines 204-235
Purpose:
Create curriculum stages from examples
Parameters:
– examples: Examples
Returns: <ast.Subscript object at 0x000001A06D1683D0>
_attempt_example(self, example, previous_examples)
Lines 237-298
Purpose:
Attempt to solve an example
Parameters:
– example: Example
– previous_examples: Previous examples
Returns: PerformanceRecord
_calculate_stage_performance(self, stage)
Lines 300-317
Purpose:
Calculate performance on current stage
Parameters:
– stage: Stage
Returns: float
_should_advance(self, stage, attempts_on_stage)
Lines 319-344
Purpose:
Determine if should advance to next stage
Parameters:
– stage: Stage
– attempts_on_stage: Attempts on stage
Returns: bool
learn_curriculum(self, examples, max_attempts_per_stage)
Lines 346-434
Purpose:
Learn through curriculum progression
Parameters:
– examples: Examples
– max_attempts_per_stage: Max attempts per stage
Returns: CurriculumResult
_calculate_recent_accuracy(self, window)
Lines 436-444
Purpose:
Calculate accuracy over recent attempts
Parameters:
– window: Window
Returns: float
Functions
demonstrate_curriculum_learning()
Lines 447-661
Purpose:
Demonstrate Curriculum Learning pattern
Key Concepts Summary
Pattern Overview
The Curriculum Learning pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
ā Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
ā Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ā
Use
.envfiles for API keys - ā Never hardcode credentials
- ā Validate environment variables on startup
2. Error Handling
- ā Graceful degradation on failures
- ā Informative error messages
- ā Logging for debugging
3. Prompt Engineering
- ā Clear, specific instructions
- ā Structured output formats
- ā Few-shot examples when beneficial
4. Code Organization
- ā Separation of concerns (class-based design)
- ā Type hints for clarity
- ā Docstrings for documentation
5. Performance Optimization
- ā Appropriate temperature settings
- ā Token budget management
- ā Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
ā
2. Load environment and dependencies
ā
3. Prepare prompt with input data
ā
4. Invoke LLM through LangChain
ā
5. Parse and validate response
ā
6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
ā Vague or ambiguous instructions
ā
Clear, specific, testable prompts
2. Error Handling
ā Ignoring API errors or rate limits
ā
Implement retry logic and graceful degradation
3. Token Management
ā Exceeding model token limits
ā
Truncate inputs, chunk processing
4. Security
ā Exposing API keys in code
ā
Use environment variables
5. Testing
ā Only testing happy paths
ā
Test edge cases, failures, malformed inputs
6. Cost Management
ā Unmonitored API usage
ā
Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 059
- Pattern 061
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Curriculum Learning pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
ā Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
šÆ Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
š Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 060_curriculum_learning.py
Total Lines: 666
Pattern Category: AI Agent Design Pattern #060
Generated from actual implementation code
For educational and reference purposes
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