Automated Labeling vs. Manual Annotation
Compare manual, automated, and hybrid (HITL) labeling—trade-offs in accuracy, speed, cost, scalability, and when to use each approach.
Cesar Miguelañez
Dynamic Prompt Behavior: Key Testing Methods
How teams use batch testing, live evaluation, A/B tests, and automated optimization loops to validate and improve dynamic prompts for reliable LLM behavior.
Open-Source Platforms for LLM Evaluation
Compare open-source LLM evaluation platforms that add observability, automated metrics, and CI/CD testing to reduce hallucinations and production errors.
How to Deploy Agentic AI in Production Safely
Discover key strategies for deploying agentic AI in production, including lessons learned, best practices, and real-world examples from industry leaders.
Complete Guide to Evaluating LLMs for Production
Discover the ultimate guide to evaluating LLMs for production, from benchmarks to real-world applications and efficiency insights.
Reconciling AI Benchmarks and Developer Productivity
Explore the gap between AI benchmark performance and real-world developer productivity in this in-depth analysis.
How to Add LLM Testing to GitHub Actions
Automate LLM testing in GitHub Actions: secure API keys, organize datasets, run Latitude evaluations, and fail builds on regressions.
LLM Prompts with External Event Triggers
Choose the right trigger—webhooks, queues, polling, or SSE—to balance latency, scalability, and reliability for production LLM workflows.
Open-Source vs Proprietary LLMs: Ethical Trade-Offs
Choosing between open-source and proprietary LLMs forces a trade-off: transparency and control versus centralized safety and convenience.
Scaling LLMs with Serverless: Cost Management Tips
Practical strategies to cut serverless LLM costs: model right-sizing, prompt and context optimization, caching, batching, routing, and real-time observability.
