Tag: rag

16 articles

• LLM Real-Time Voice Input in Production: Low-Latency Recognition with VAD Segmentation, Partial/Final Reconciliation, and Audio Contracts

  Real-time voice applications face challenges beyond model accuracy. This article systematically breaks down how to build an observable, rollback-capable low-latency speech recognition pipeline—from audio format contracts and VAD segmentation strategies to Partial/Final state reconciliation, timestamp replay, and real-world noise evaluation. Designed for LLM, generative AI, and real-time Agent practitioners.

  07/13/2026 LLM / real-time voice / speech recognition / VAD / production engineering / Agent / RAG

• LLM LoRA Adapter Cache in Production: Stabilizing Multi-Tenant Fine-Tuning Services with Heat Tiering, Cold-Start Budgets, and Eviction Policies

  Production strategies for LoRA adapter cache management in multi-tenant fine-tuning services: heat tiering, cold-start budgets, eviction policies, and a governance framework for stable model serving.

  07/09/2026 LLM Serving / LoRA / Adapter Cache / Multi Tenant / Model Serving / GPU / Fine Tuning / RAG / vLLM

• LLM Embedding Index Migration in Production: Avoiding Recall Drift with Dual-Writes, Alias Switching, and Recall Gates

  A systematic guide to zero-downtime embedding index migration covering dual-writes, alias switching, backfill validation, and recall gates to prevent recall drift during model upgrades.

  07/08/2026 LLM / Embedding / Vector Search / RAG / Production

• LLM Tool Result Caching in Production: Reducing Agent External Call Costs with Idempotency Keys, TTL, and Invalidation Strategies

  A practical guide to caching external tool results for LLM agents, covering idempotent key design, TTL semantics, event-driven invalidation, state isolation, audit replay, and a pre-launch checklist to reduce API costs and latency while avoiding cache pollution and cross-tenant leaks.

  07/07/2026 LLM Agent / Tool Calling / Cache / Production Engineering / Idempotency Key / Cache Invalidation / RAG

• LLM Reranker Production Evaluation: Guarding RAG Retrieval Quality with Golden Query Sets, nDCG, and Latency Budgets

  A deep dive into production evaluation methodologies for LLM Rerankers in RAG systems, covering golden query set construction, nDCG and MRR ranking metrics, candidate set budget optimization, latency and cost governance, canary release gates, and rollback strategies to prevent retrieval ranking degradation.

  07/07/2026 LLM / RAG / Reranker / Retrieval Evaluation / Production AI

• LLM Hybrid Retrieval Routing in Production: Stabilizing RAG Recall with Query Classification, RRF, and Fallback Strategies

  A deep dive into production practices for hybrid retrieval routing in LLM RAG systems, covering query classification, BM25 and vector search fusion, RRF ranking, weight tuning, fallback governance, and a go-live checklist to reduce missed recalls and retrieval strategy failures.

  07/05/2026 LLM / RAG / Hybrid Retrieval / RRF / Query Routing / Recall Strategy

• LLM Long Video Understanding in Production: Stabilizing QA Quality with Frame Sampling, Timestamp Indexing, and Clip Replay

  This article covers engineering practices for production-grade long video QA, including frame sampling, shot segmentation, subtitle fusion, timestamp indexing, clip replay, evidence verification, and launch checklists to reduce missed keyframes and temporal ordering errors.

  07/05/2026 LLM / video understanding / multimodal AI / production engineering / RAG / frame sampling / timestamp indexing

• LLM Retrieval Index Update in Production: Using Blue-Green Index and Incremental Backfill to Prevent RAG Quality Regression

  This article explains how to govern RAG retrieval index updates, covering blue-green indexes, dual-write, incremental backfill, alias switching, quality sampling, and rollback strategies to help teams reduce retrieval quality regression risk when documents, embedding models, or chunking rules change.

  07/04/2026 LLM / RAG / Vector Search / Embedding / Production Engineering

• LLM Document Vision Extraction in Production: Stabilizing PDF Parsing Quality with Layout Chunking and Confidence Auditing

  A practical guide to using multimodal LLMs for PDF and scanned document parsing in production. Covers layout chunking, table extraction, confidence auditing, human review, and launch validation to reduce hallucinations, missed fields, and misaligned parsing for complex documents like contracts, financial reports, and work orders.

  07/04/2026 llm / multimodal / document-ai / pdf-parsing / production / rag / layout-analysis / vision-language-model

• Embedding & Reranker Production Evaluation: Preventing Retrieval Regression with Offline Replay and Shadow Traffic

  A systematic guide to evaluating retrieval quality after deploying Embedding and Reranker models. Covers offline replay, shadow traffic, hybrid search, metric design, and regression handling to prevent silent quality degradation.

  07/02/2026 Embedding / Reranker / RAG / Hybrid Search / Retrieval Evaluation / Shadow Traffic / Offline Replay

• LLM Observability in Production: Tracing Agent Call Chains with OpenTelemetry GenAI Semantic Conventions

  A systematic guide to building observability for LLM and Agent applications using OpenTelemetry GenAI semantic conventions. Covers trace chains, tokens, tool calls, privacy governance, and pre-launch checks to help teams pinpoint latency, cost, and quality issues.

  06/30/2026 llm / observability / opentelemetry / ai-agent / rag / genai / agent-tracing

• Prefix Caching in Production: A Complete Guide to Reusing KV Cache for RAG and Agent Services

  A systematic guide to Prefix Caching for reusing KV Cache of shared prompts and contexts, reducing TTFT, inference costs, and redundant prefill in RAG, Agent, and long-document QA scenarios. Includes engineering checklists for vLLM, OpenAI API, and SGLang.

  06/30/2026 LLM Serving / KV Cache / RAG / Agent Infrastructure / Prefix Caching / Inference Optimization

• Agent Memory Engineering: Building a Governable Memory Layer for LLM Agents

  From short-term sessions to long-term memory, systematically deconstruct the write, manage, read, forget, and evaluate mechanisms for LLM Agents. Build a production-ready memory system that avoids the pitfalls of an uncontrollable vector store.

  06/30/2026 LLM Agent / Agent Memory / RAG / LangGraph / LlamaIndex / LangMem / MemGPT / Vector Database / Memory Governance

• Contextual Retrieval in Production: Making RAG Chunks Retrievable with Context

  A practical guide to Contextual Retrieval: indexing with context, hybrid search, reranking, and deployment checks to reduce missed recall and improve answer traceability.

  06/30/2026 RAG / Contextual Retrieval / Hybrid Search / Reranking / Vector Search

• LLM-as-a-Judge in Production: Rubrics, Biases, and a Closed-Loop Regression Testing System

  A practical guide to deploying LLM-as-a-Judge in production for RAG, Agent, and content generation systems, covering scoring modes, rubric design, bias sources, online regression, human review, and threshold governance.

  06/30/2026 LLM Evaluation / LLM-as-a-Judge / RAG / Agent / AI Engineering

• Prefill-Decode Disaggregation in Practice: Taming Latency for Long-Context LLM Serving

  Long contexts and agentic requests shift LLM inference bottlenecks from single-card throughput to tail-latency governance. This post dives into Prefill-Decode disaggregation, KV cache transfer, resource planning, and a pre-launch checklist to move LLM serving from 'working' to stable and scalable.

  06/29/2026 LLM Serving / Prefill Decode / KV Cache / Inference Optimization / Long Context / Tail Latency / RAG / Agent