Blog

• How to Build High-Performance LLM Systems?
  2025-10-09

  A practical, end-to-end guide to making LLMs feel instant: what to measure (TTFT, TPOT, P99), where the time goes (prefill vs. decode, KV cache), and which optimizations actually move the needle (PagedAttention, FlashAttention, quantization, batching, caching) — with a chatbot-style pipeline as the running example.

  LLM InferencePerformanceLatencyvLLMPagedAttentionFlashAttentionQuantizationSpeculative Decoding
• Mastering Multi-Vector Embeddings: Beyond Traditional Semantic Search
  2025-10-09

  Multi-vector embeddings represent a paradigm shift in semantic retrieval — moving from single compressed vectors to fine-grained token-level understanding. This article explains the architecture, math, and practical optimizations behind modern multi-vector systems.

  Information RetrievalEmbeddingsColBERTVector Search
• MUVERA: Making Multi-Vector Retrieval as Fast as Single-Vector Search
  2025-10-09

  Google Research’s MUVERA bridges the gap between multi-vector and single-vector retrieval, offering near-ColBERT accuracy at a fraction of the latency. Here’s how it works and why it matters for the next generation of semantic search systems.

  Information RetrievalEmbeddingsMulti-VectorMUVERA
• Understanding Rotary Positional Embeddings (RoPE)
  2025-10-08

  A deep yet intuitive explanation of Rotary Positional Embeddings — why they were invented, how they work mathematically, and how modern long-context models extend them.

  LLMTransformersAttentionEmbeddings
• Why Rerankers Matter in RAG: Fixing the Two-Tower Blind Spot
  2025-10-04

  Semantic search retrieves similar texts, not necessarily relevant ones. Rerankers fix this by introducing true query–document interaction. Here’s why every production RAG pipeline needs them.

  RAGInformation RetrievalLLMRerankingCross-Encoder
• Speculative Decoding Explained: Accelerating LLM Inference Without Quality Loss
  2025-10-04

  A deep technical dive into speculative decoding: how it works, why it speeds up large language model inference, and what trade-offs it introduces.

  LLM InferenceSpeculative DecodingOptimizationParallelization
• Quantization for LLM Inference
  2025-10-02

  A clear, professional overview of quantization in large language models: fundamentals, precision formats, calibration, frameworks, and system-level considerations.

  LLMQuantizationInferenceOptimization
• Measuring LLM Inference Performance: Beyond Tokens per Second
  2025-09-24

  Tokens per second alone does not define LLM inference performance. This guide covers TTFT, TPOT, P99 latency, Goodput, RAG pipeline timing, optimization strategies, and real benchmarking code for production systems.

  LLMInferenceLatencyML EngineeringPerformance
• PagedAttention: Fixing the Real Bottleneck in LLM Serving
  2025-09-24

  The real bottleneck in LLM serving isn’t compute—it’s memory. This post explains how KV cache dominates GPU usage, why fragmentation kills throughput, and how vLLM’s PagedAttention delivers order-of-magnitude speedups, with math, benchmarks, and production insights.

  LLMInferencePerformancePagedAttentionvLLM
• FastVLM: Accelerating Vision-Language Models with Efficient Visual Encoders
  2025-09-16

  Apple’s FastVLM introduces a hierarchical hybrid vision encoder that balances accuracy, speed, and token efficiency in multimodal AI systems.

  Vision-Language ModelsMultimodal AIEfficiencyFastVLMDeep Learning
• A Survey of Reinforcement Learning for Large Reasoning Models
  2025-09-16

  An overview of the survey 'A Survey of Reinforcement Learning for Large Reasoning Models' (arXiv:2509.08827). Covers reward design, policy optimization, sampling strategies, core challenges, applications, and future directions.

  Reinforcement LearningLLMReasoningAI SurveyRLHF
• Limits of Single-Vector Embeddings & Why BM25 Still Matters
  2025-09-07

  A walkthrough from BoW to BM25 to embeddings, leading into the LIMIT paper. Explains why single-vector embeddings face structural limits, why BM25 remains crucial, and how hybrid retrieval strategies outperform standalone methods.

  RetrievalEmbeddingsBM25LIMIT datasetInformation Retrieval
• RL’s Razor: Why On-Policy Reinforcement Learning Forgets Less
  2025-09-07

  An accessible yet professional walkthrough of catastrophic forgetting, KL divergence, and why on-policy RL preserves prior knowledge better than supervised fine-tuning.

  Reinforcement LearningCatastrophic ForgettingKL DivergenceFine-TuningMachine Learning
• Are LLMs Truly Bayesian?
  2025-09-06

  An in-depth explanation and analysis of the paper 'LLMs are Bayesian, in Expectation, not in Realization.' It explores why LLMs mimic Bayesian inference only on average, breaking strict consistency in single realizations.

  LLMBayesianIn-Context LearningChain-of-Thought
• Why Do Language Models Hallucinate?
  2025-09-06

  An exploration of the OpenAI paper 'Why Language Models Hallucinate' using Bloom’s Taxonomy. Explains why hallucinations are structural, how training incentives encourage overconfidence, and what system-level changes can reduce them.

  LLMHallucinationBloom's TaxonomyAI Safety
• Graph–Text Fusion for Ad Recommendations: Cold-Start, Reranking, and Real-World Ops
  2025-09-01

  A multilingual recommendation pipeline that fuses Node2Vec graph signals with text semantics, served via Qdrant/HNSW, with dynamic cold-start handling and optional reranking.

  Recommender SystemsGraph EmbeddingsMultilingualRAGQdrantNode2VecReranking
• Manga Panel Search with Visual Similarity, Reranking, and VLM Explanation
  2025-08-29

  A multimodal RAG system that retrieves and explains manga scenes using visual and textual queries—built with Qdrant, MaxSim reranker, and MiniCPM-V.

  Multimodal RAGVision Language ModelMangaRerankerQdrant
• Designing a Multilingual Semantic Search Architecture
  2025-08-13

  A practical blueprint for multilingual semantic search: pipeline, retriever + reranker + judge, experiments, and trade-offs.

  IRSemantic SearchReranking
• Language Models Are Like Playing Minesweeper
  2023-10-13

  Why evaluating LLMs is hard: prompt sensitivity, construct validity, contamination, and reproducibility—plus why open source matters.

  LLMsEvaluationOpen SourcePolicy
• Attention: The Only Thing You Need
  2023-01-13

  A practical walk-through of Transformers: sequence transduction, the original paper, attention mechanics, and model families.

  TransformersAttentionDeep LearningNLP
• The Cure for the Curse of Dimensionality: PCA
  2022-08-21

  Why high-dimensional data is hard, how PCA helps, and a step-by-step guide with scikit-learn.

  PCADimensionality ReductionMachine Learning