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Token optimization: three production patterns that reduce LLM costs by 70%
API-level caching, semantic similarity-based caching, and dynamic compression with LLMLingua form a layered approach to token reduction. Each pattern targets different inefficiencies in the prompt processing pipeline.
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Hierarchical signal tuning: optimizing components before fusion
Fusion algorithms like linear combination or RRF cannot fix poor input signals. Effective hybrid search requires a bottom-up optimization strategy: tuning field weights within BM25 and embedding strategies within dense components before attempting to merge them.
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Jensen-Shannon divergence for meaningful clustering
Silhouette score validates geometry, not meaning. Using Jensen-Shannon divergence to measure feature distribution divergence bridges the gap between mathematical separation and interpretability.
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Hybrid intent classification: compact-encoder-first routing for production systems
Production chatbots route most requests through fast compact encoder classifiers, escalating to LLMs only on low-confidence queries. This hybrid architecture mitigates the latency and cost overheads of monolithic LLM solutions, achieving significant speed gains while preserving high classification accuracy.
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Few-shot prompt ordering: the impact of example position
Investigating positional bias in few-shot prompting. While "Lost in the Middle" suggests boundary importance, the specific ordering of examples remains an important factor for performance stability.