Practical deployment guides for multi-agent workflows, semantic vector stores, speculative decoding, and quantization.
Architecting low-latency Retrieval-Augmented Generation systems with hybrid BM25 search, Hypothetical Document Embeddings, and cross-encoders.
Empirical analysis of Generative Engine Optimization (GEO) strategies, measuring citation frequency in ChatGPT, Claude, and Perplexity using standardized llms.txt manifests and Schema.org JSON-LD graphs.
Empirical benchmarks comparing speculative decoding performance across vLLM, TensorRT-LLM, and SGLang using Llama-3.3-70B and quantized draft models.
A deep empirical comparison of Hierarchical Navigable Small World graphs versus Inverted File Product Quantization across 100M vector workloads.
Evaluating activation-aware weight quantization versus generalized post-training quantization on edge accelerators and consumer workstations.