17 articles
Red teaming loses value if run only once before launch. This article shows how to turn every confirmed security failure into a repeatable regression asset using versioned attack corpora, deterministic scoring, and CI quality gates with PyRIT, garak, and Promptfoo.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A practical guide to Contextual Retrieval: indexing with context, hybrid search, reranking, and deployment checks to reduce missed recall and improve answer traceability.
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.
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.