Resource optimization
Not every request needs GPT-4. The resource optimization pattern analyzes task complexity and routes simple tasks to cheaper/faster models while reserving expensive models for complex work. This can cut costs by 60-80% with minimal quality loss.
Tiered model routing
In practice, 60-80% of requests are simple enough for a fast, cheap model. If those requests are 10x cheaper to process, your overall cost drops dramatically with minimal quality impact on the easy tasks.
class ResourceOptimizer:
def analyze_complexity(self, task):
"""Score task complexity 1-10."""
score = 0
word_count = len(task.split())
score += 3 if word_count > 50 else 2 if word_count > 20 else 1
complex_keywords = [
"analyze", "comprehensive", "compare", "evaluate"
]
for keyword in complex_keywords:
if keyword.lower() in task.lower():
score += 2
return min(10, score)
def choose_strategy(self, task, complexity):
if complexity <= 3:
return "simple" # Cheap, fast model
elif complexity <= 6:
return "standard" # Mid-tier model
else:
return "complex" # Premium model + tools
def process_task(self, task):
complexity = self.analyze_complexity(task)
strategy = self.choose_strategy(task, complexity)
# Route to appropriate processing pipeline
if strategy == "simple":
return self.process_simple(task)
elif strategy == "standard":
return self.process_standard(task)
else:
return self.process_complex(task)Complexity scoring determines which processing strategy to use.
Quiz: Quiz
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Matching exercise: Match resource optimization concepts
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Flashcards: Flashcards
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You can now route tasks to the right model tier based on complexity. That wraps up our knowledge and communication module.
Checkpoint: Knowledge & communication check
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