Local logs won’t cut it in production. In this post, you’ll learn how to forward structured logs from your AI automation to a central system like Graylog, and set up real-time alerts to catch failures before users do. A simple Fluent Bit config and JSON logging format make it easy to get started.
Better retrieval leads to better answers in RAG. It’s not just about what you feed your LLM (CONTEXT), but how you help your retrieval system find it in the first place (context). Learn how adding other clues can make your similarity search smarter… and why storing them as metadata in your vector database can also help.
Make your LLM automations more reliable by logging both prompts and responses. This simple change gives you visibility into what your model is doing, crucial for debugging, auditing, and improving over time. In this post, we’ll show you how to log structured JSON output to a file, making it easy to parse and monitor.
Moving your no-code AI automation into containers makes it more stable, portable, and ready for production. In this post, we walk through running OpenWebUI, N8N, Ollama, and Postgres together using Docker Compose, so your entire automation stack works like one unified app.
Most AI projects fail before they ever reach real business use. The problem isn’t the technology; it’s the way they’re built. A demo AI might impress in the lab, but a production AI needs to be secure, scalable, reliable, and trusted by users. Learn why building for production from day one saves time, money, and frustration… and how you can start with practical steps today.
