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Architecture design: a constraint-satisfaction approach
Methodology for reducing the architectural search space through hierarchical constraint definition: problem, boundaries, and trade-offs.
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Classification with LLMs: getting accurate probabilities from structured output
Verbalized confidence in JSON schema provides fast probability estimates for classification tasks. Optimization patterns improve calibration.
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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.