#llmops
17 articles tagged llmops.
Prompt Engineering Best Practices for Enterprise Teams
Beyond \"be clear and specific\" — the prompt engineering techniques enterprise teams actually ship: system-prompt architecture, few-shot design, retrieval grounding, structured outputs, and iterating with evals.
LLM Observability in Production: Logs, Traces, and Metrics That Matter
What to measure, log, and alert on in a production LLM app — latency, token usage, error rates, retrieval quality — and the tools and patterns to get there.
LLM Token Budgeting: Sizing Prompts, Context, and Cost
A practical guide to budgeting tokens in LLM apps — how tokens map to cost and context limits, how to estimate prompt size, and the levers that keep spend predictable.
Building an AI Gateway with Apigee (LLM Proxy)
Put Apigee in front of your LLM providers to centralize API keys, cost control, quotas, logging, and guardrails. A practical blueprint for an enterprise AI gateway.
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.