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You’ve probably used AI earlier today. Maybe you asked it to debug a function, generate a test case, or explain a legacy codebase you just inherited. But here’s the thing: you didn’t just type a question and get an answer. You explained your problem, shared background context, pasted code snippets, clarified what you meant, then refined the output until it was actually useful. In other words, you were context engineering.

Happy New Year, everyone! 2025 was another exciting year filled with impressive AI advancements. As you might expect, some significant cost changes accompanied these new developments. Because reflecting on the past is one of the best ways to prepare for an even stronger future, here’s your end-of-2025 headline round-up, complete with what you can expect going forward into 2026: Get caught up on the details below.

We’ve written before about the advantages of training an AI SRE on real telemetry data rather than generic Kubernetes documentation. We’ve explained why RAG augmentation based on actual high-scale workload patterns produces better results than LLMs trained on generic scenarios or forum threads. The theory makes sense, the architecture is sound, and the approach is defensible.