#llmops
13 articles tagged llmops.
What's New in Modern Java — and How to Build AI With It
Modern Java (21→25) brings virtual threads, structured concurrency, records, and the FFM API. Here's what's new and how it powers AI apps.
Context Engineering: The Real Skill Behind Reliable LLM Apps
What you put in the context window matters more than prompt wording. A practical guide to context engineering — the budget, techniques, and failure modes.
Observability for LLM Systems in Production
How to instrument, monitor, and alert on LLM apps — distributed tracing, cost dashboards, quality metrics, and incident response for AI systems.
Zero to Production: Building Your First Enterprise LLM Application
A four-phase guide to taking an LLM prototype to a production enterprise app — RAG, caching, observability, cost control, and multi-model routing.
Testing AI Applications: From Prompts to Production
A complete testing strategy for LLM apps — unit-testing prompts, building eval pipelines, regression-testing quality, and load-testing AI endpoints.
Prompt Caching: Cut Your LLM Costs by 80%
A practical guide to Anthropic and OpenAI prompt caching — how it works and how to implement it in Spring AI to cut latency and API costs.
Building Fault-Tolerant AI Systems
Resilience patterns for production AI — circuit breakers, fallback chains, and graceful degradation so systems survive provider outages and rate limits.
LLM Inference Explained
How large language models generate responses — from tokenisation to transformer attention — and what this means for building production AI systems.
RAG Chunking Strategies That Actually Improve Retrieval
Your RAG quality is capped by how you chunk. A practical comparison of fixed, recursive, semantic, and structural chunking, with sizing and overlap tips.
AI Gateways: Managing LLM Traffic in the Enterprise
As LLM usage spreads across an org, you need a control point. What an AI gateway is, the problems it solves, and the capabilities to look for.
Evaluating RAG Systems: Metrics That Catch Real Failures
You can't improve a RAG system you can't measure. The metrics that matter — faithfulness, relevance, context precision and recall — and how to build an eval loop.
Fine-tuning vs RAG vs Prompting: How to Choose
Teams reach for fine-tuning when they need RAG, or RAG when a better prompt would do. A decision framework for choosing the right approach by problem type.
Securing LLM Apps: Guardrails for Production
LLM features open new attack surfaces — prompt injection, data leakage, unsafe tool use. A practical guardrails checklist for shipping AI to production safely.