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Context Window Optimization

Maximize LLM performance while minimizing costs by intelligently managing context windows. Reduce token usage by up to 70% without losing accuracy.

Dynamic Context Management
class ContextOptimizer:
    def __init__(self, max_tokens=8192):
        self.max_tokens = max_tokens
        self.compressor = TextCompressor()
        
    def optimize_context(self, messages, system_prompt):
        # Priority-based context selection
        essential_context = self.extract_essential(messages)
        
        # Compress verbose sections
        compressed = self.compressor.compress(
            essential_context,
            preserve_keywords=True
        )
        
        # Sliding window for conversation history
        return self.apply_sliding_window(
            compressed,
            window_size=self.calculate_optimal_window()
        )
Compression Techniques
  • Summarization of old messages
  • Keyword extraction
  • Redundancy removal
Cost Savings
70%

Average token reduction with intelligent compression

References
  1. [1] arXiv. "Efficient LLM Inference" (2024)
  2. [2] Hugging Face. "GPU Inference Optimization" (2024)
  3. [3] NVIDIA. "LLM Inference Optimization" (2024)