<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Posts on Bharat Singh</title><link>https://bsbhadwal.com/posts/</link><description>Recent content in Posts on Bharat Singh</description><image><title>Bharat Singh</title><url>https://bsbhadwal.com/images/bharat-singh.jpg</url><link>https://bsbhadwal.com/images/bharat-singh.jpg</link></image><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 13 Oct 2026 00:00:00 +0530</lastBuildDate><atom:link href="https://bsbhadwal.com/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>Reciprocal Rank Fusion vs. Cross-Encoders: Designing the Sub-50ms Reranking Funnel</title><link>https://bsbhadwal.com/posts/reciprocal-rank-fusion-vs-cross-encoders-reranking-funnel/</link><pubDate>Mon, 12 Oct 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/reciprocal-rank-fusion-vs-cross-encoders-reranking-funnel/</guid><description>Designing high-throughput hybrid retrieval reranking funnels combining Reciprocal Rank Fusion (RRF) and Cross-Encoder neural scoring under sub-50ms SLAs.</description></item><item><title>Deterministic Guardrails in Stateful Multi-Agent Loops: Preventing Infinite Trajectory Drift</title><link>https://bsbhadwal.com/posts/deterministic-guardrails-stateful-multi-agent-loops/</link><pubDate>Wed, 16 Sep 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/deterministic-guardrails-stateful-multi-agent-loops/</guid><description>Engineering deterministic state-machine guardrails in autonomous multi-agent loops to prevent trajectory drift, infinite loops, and token budget exhaustion.</description></item><item><title>Context Caching vs. Semantic Caching: Latency Trade-Offs Across High-Throughput Agent Workflows</title><link>https://bsbhadwal.com/posts/context-caching-vs-semantic-caching-latency-physics/</link><pubDate>Wed, 19 Aug 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/context-caching-vs-semantic-caching-latency-physics/</guid><description>Architectural comparison of LLM context caching and semantic caching. Analyzing TTFT, token economics, cache hit ratios, and multi-agent latency budgets.</description></item><item><title>ColBERT and Late Interaction at Scale: Why Multi-Vector Retrieval Strains Production Memory</title><link>https://bsbhadwal.com/posts/colbert-late-interaction-at-scale-memory-bandwidth-physics/</link><pubDate>Wed, 22 Jul 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/colbert-late-interaction-at-scale-memory-bandwidth-physics/</guid><description>Deep architectural analysis of ColBERT late interaction multi-vector retrieval, memory bandwidth physics, PLAID indexing, and sub-50ms latency SLAs.</description></item><item><title>Evaluating RAG Without Ground Truth: Production Pitfalls of LLM-as-a-Judge</title><link>https://bsbhadwal.com/posts/evaluating-rag-without-ground-truth-llm-as-a-judge-pitfalls/</link><pubDate>Mon, 15 Jun 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/evaluating-rag-without-ground-truth-llm-as-a-judge-pitfalls/</guid><description>Production analysis of evaluating enterprise RAG pipelines without human ground truth, mitigating LLM-as-a-judge bias, and building empirical synthetic test sets.</description></item><item><title>From Semantic Nanopublications to Knowledge Graphs: What 15 Years of Machine-Readable Data Taught Us About RAG Grounding</title><link>https://bsbhadwal.com/posts/semantic-nanopublications-to-knowledge-graphs-rag-grounding/</link><pubDate>Wed, 20 May 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/semantic-nanopublications-to-knowledge-graphs-rag-grounding/</guid><description>Applying 15 years of semantic web nanopublication principles and RDF graph triples to establish verifiable provenance and mitigate hallucination in enterprise RAG pipelines.</description></item><item><title>Shrinking Billion-Scale Vector Indexes: Combining Matryoshka Embeddings with Binary Quantization</title><link>https://bsbhadwal.com/posts/shrinking-billion-scale-vector-indexes-matryoshka-embeddings-binary-quantization/</link><pubDate>Sat, 18 Apr 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/shrinking-billion-scale-vector-indexes-matryoshka-embeddings-binary-quantization/</guid><description>Techniques for shrinking billion-scale vector indexes using Matryoshka Representation Learning (MRL) and 1-bit binary quantization with Hamming distance rescoring.</description></item><item><title>The Retrieval Fallacy: Why Massive Context Windows Don't Eliminate the Need for IR</title><link>https://bsbhadwal.com/posts/the-retrieval-fallacy-why-more-tokens-dont-equal-better-context/</link><pubDate>Thu, 12 Mar 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/the-retrieval-fallacy-why-more-tokens-dont-equal-better-context/</guid><description>Why 1M+ token context windows do not replace Information Retrieval. Examining needle-in-a-haystack decay, attention dilution, and cost economics.</description></item><item><title>The Paradigm Shift to Late Chunking in Retrieval-Augmented Generation: A Technical and Comparative Analysis</title><link>https://bsbhadwal.com/posts/late-chunking-in-retrieval-augmented-generation/</link><pubDate>Mon, 09 Feb 2026 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/late-chunking-in-retrieval-augmented-generation/</guid><description>Technical breakdown of Late Chunking in RAG architectures. Contrasting Jina AI token mean pooling and Voyage AI contextualized embeddings against naive chunking.</description></item><item><title>The Unit Economics of Inference: When to Kill Third-Party AI APIs</title><link>https://bsbhadwal.com/posts/the-unit-economics-of-hosted-inference-vs-apis/</link><pubDate>Wed, 19 Nov 2025 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/the-unit-economics-of-hosted-inference-vs-apis/</guid><description>Financial and latency model analyzing the tipping point for migrating from third-party LLM APIs to self-hosted vLLM/TGI GPU clusters at enterprise scale.</description></item><item><title>Why Workflow DAGs Break at 2,500+ Tenants: Moving Multi-Tenant RAG from Batch Orchestration to Event-Driven Pub/Sub</title><link>https://bsbhadwal.com/posts/why-workflow-dags-break-at-scale-pubsub-vs-prefect/</link><pubDate>Thu, 16 Oct 2025 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/why-workflow-dags-break-at-scale-pubsub-vs-prefect/</guid><description>Why static workflow DAG orchestrators collapse under multi-tenant RAG workloads at scale, and how event-driven Pub/Sub message buses solve tenant isolation.</description></item><item><title>Building Self-Discovering AI Agents: Dynamic Tool Discovery with gRPC Reflection and LangGraph</title><link>https://bsbhadwal.com/posts/architecting-dynamic-self-discovering-ai-agents-a-langgraph-and-grpc-reflection-prototype/</link><pubDate>Fri, 22 Aug 2025 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/architecting-dynamic-self-discovering-ai-agents-a-langgraph-and-grpc-reflection-prototype/</guid><description>Architecting self-discovering AI agents using gRPC Server Reflection and LangGraph for dynamic schema discovery without hardcoded API tools.</description></item><item><title>The Scaling Dilemma: An Analysis of Tool Quantity and its Impact on ReAct Agent Performance</title><link>https://bsbhadwal.com/posts/the-react-scaling-dilemma/</link><pubDate>Fri, 15 Aug 2025 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/the-react-scaling-dilemma/</guid><description>Comparative architectural analysis of how expanding tool quantities degrades ReAct agent reasoning accuracy, burns latency budgets, and necessitates hierarchical routing.</description></item><item><title>The Cognitive Tax of Trivial Helpers: Lessons in Interface Design from the Linux Kernel</title><link>https://bsbhadwal.com/posts/think-for-yourself-lessons-from-linus/</link><pubDate>Tue, 12 Aug 2025 00:00:00 +0530</pubDate><guid>https://bsbhadwal.com/posts/think-for-yourself-lessons-from-linus/</guid><description>Analyzing Linus Torvalds rejection of trivial helper abstractions in the Linux kernel and its lessons for clean software and AI interface design.</description></item></channel></rss>