GPT-5.4 makes prompt caching automatic with no configuration required. This part covers how OpenAI’s caching works under the hood, how to structure prompts for maximum hit rates, how the new Tool Search feature reduces agent token costs, and a full production C# implementation with cost tracking.
Category: AI
Prompt Caching with Claude Sonnet 4.6: cache_control Breakpoints, TTL Strategies, and Node.js Production Implementation
Claude Sonnet 4.6 gives developers explicit control over prompt caching through cache_control breakpoints. This part covers how to structure your prompts, configure TTL, use multi-breakpoint strategies, and implement a production-ready caching layer in Node.js.
Prompt Caching and Context Engineering in Production: What It Is and Why It Matters in 2026
Prompt caching is one of the most impactful yet underused techniques in enterprise AI today. This first part of the series explains what it is, how it works under the hood, and why it should be a default part of your production AI architecture in 2026.
Group Code: The VS Code Extension Built for Vibe Coders Who Move Fast and Build Things
Vibe coders build fast and ship faster — but that speed creates messy codebases. Group Code is the VS Code extension that keeps up with you, organizing your code by what it does rather than where it lives.
The 2026 Developer Landscape: Languages, Tools, and the Agentic Coding Revolution
From Rust taking over systems programming to TypeScript becoming the universal default, the 2026 developer landscape is defined by performance, safety, and AI-assisted workflows. Here is what every developer needs to know right now.
Building a Complete LLMOps Stack: From Zero to Production-Grade Observability
Seven posts, seven production systems. This final installment assembles every piece — distributed tracing, metrics, evaluation, prompt versioning, RAG observability, and cost governance — into one reference architecture with a phased implementation checklist you can start using this week.
Cost Governance and FinOps for LLM Workloads
In 2026, inference accounts for 85% of enterprise AI budgets — and agentic loops mean costs can spiral quadratically from a single runaway task. This post builds a complete LLM cost governance system: per-feature attribution, tenant budgets with hard limits, spend anomaly detection, and the optimization levers that cut bills without touching quality.
RAG Pipeline Observability: Tracing Retrieval, Chunking, and Embedding Quality
A RAG pipeline has five distinct places it can fail before the LLM ever sees your context. This post instruments every stage — query embedding, vector search, document ranking, context assembly, and generation — with OpenTelemetry spans and quality metrics, in Node.js, Python, and C#.
The LLM Landscape in March 2026: Open Source Catches Up, Local AI Goes Mainstream
In the span of a single week in early March 2026, more than twelve major AI models shipped across language, video, and spatial reasoning domains.
Prompt Management and Versioning: Treating Prompts as Production Code
Prompt changes are production changes. A wording edit at 3pm on a Friday can silently degrade thousands of responses with no error signal. This post builds a production-grade prompt management system with versioning, A/B testing, quality gates, and rollback in Node.js, Python, and C#.
