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Building a Modern Search Ranking Stack: From Embeddings to LLM-Powered Relevance

Search is no longer a string-matching problem. A query for "wireless headphones" on a product search engine is not just about finding items containing those two words — it is about surfacing the best result based on semantic relevance, product quality, user preferences, and real-time availability. The gap between BM25 keyword matching and what users actually expect has forced a complete rethinking of search architecture.

This post walks through the anatomy of a modern search ranking stack: a multi-stage pipeline that combines sparse lexical retrieval, dense semantic embeddings, reciprocal rank fusion, cross-encoder reranking, and LLM-powered listwise ranking. I built a working demo that benchmarks each stage on the Amazon ESCI product search dataset — proving the value of every layer with real numbers.

The Cognitive Engine: Choosing the Right Reasoning Loop

Part 1 of the Engineering the Agentic Stack series

Building production AI agents is no longer about prompt engineering—it's about system engineering. The difference between a demo that impresses and a product that delivers comes down to one critical decision: how your agent thinks.

This post introduces three reasoning loop architectures and shows you how to choose between them. I'll use a production-grade Market Analyst Agent as the running example, with code you can use today.

mHC: How DeepSeek Scaled Residual Connections Without Breaking Training

The success of modern deep learning rests on a deceptively simple idea: the residual connection. Yet after a decade of stacking layers deeper and deeper, researchers at DeepSeek asked a different question—what if we could scale width instead? Their answer, Manifold-Constrained Hyper-Connections (mHC), solves a fundamental instability problem that has blocked this path for years.

This article breaks down the evolution from basic residuals to mHC, explaining why each step was necessary and what makes DeepSeek's solution work at scale.

Enterprise RAG Challenge 3: Winning Approaches for Autonomous AI Agents

The Enterprise RAG Challenge 3 (ECR3) just concluded, and the results reveal powerful insights for building production-grade AI agents. Out of 524 teams and over 341,000 agent runs, only 0.4% achieved a perfect score—making this one of the most demanding benchmarks in the field. With the leaderboard and solution write-ups now public, I analyzed the winning approaches to distill the patterns that separated top performers from the rest.

This article breaks down what ECR3 is, what tasks were involved, and how the best teams solved them.

The Complete Guide to LLM Fine-Tuning in 2025: From Theory to Production

Fine-tuning has become the secret weapon for building specialized AI applications. While general-purpose models like GPT-4 and Claude excel at broad tasks, fine-tuning transforms them into laser-focused experts for your specific domain. This guide walks you through everything you need to know—from understanding when to fine-tune to deploying your custom model.

Schema-Guided Reasoning on vLLM — Turning LLMs into Reliable Business Logic Engines

TL;DR

Schema-Guided Reasoning (SGR) is a technique that forces LLMs to reason through predefined steps by enforcing structured output schemas. Instead of hoping the model follows your formatting instructions, you guarantee it with constrained decoding. Combined with vLLM's xgrammar backend, you get 100% valid JSON output with near-zero latency overhead.

The problem: You build an LLM-powered agent. It works in demos. In production, it outputs malformed JSON, skips reasoning steps, and gives inconsistent responses. You add retry loops, validation layers, larger models. Costs explode.

The fix: Define your reasoning topology as a Pydantic schema. Let xgrammar enforce it at the token generation level. The LLM physically cannot produce invalid output.

LoRAX Playbook - Orchestrating Thousands of LoRA Adapters on Kubernetes

Serving dozens of fine-tuned large language models used to mean provisioning one GPU per model. LoRAX (LoRA eXchange) flips that math on its head: keep a single base model in memory and hot-swap lightweight LoRA adapters per request.

This guide shows you how LoRAX achieves near-constant cost per token regardless of how many fine-tunes you're serving. We'll cover:

  • What LoRA is and why it's a game-changer.
  • LoRAX vs. vLLM: When to use which.
  • Kubernetes Deployment: A production-ready Helm guide.
  • API Usage: REST, Python, and OpenAI-compatible examples.

Context Engineering in the Agentic‑AI Era — and How to Cook It

TL;DR

Context engineering (the context layer) is the pipeline that selects, structures, and governs what the model sees at the moment of decision: Instructions, Examples, Knowledge, Memory, Skills, Tools, Guardrails. Agentic systems live or die by this layer. Below is a field‑tested blueprint and patterns.

The problem: You build an agent. It works in demos, fails in production. Why? The model gets the wrong context at the wrong time—stale memory, irrelevant docs, no safety checks, ambiguous instructions.

The fix: Design the context layer deliberately. This guide shows you how.