Topic
LLMOps, Evaluation, and Model Choice
The model is rarely the hard part. Keeping a language-model system reliable in production is: versioning prompts, evaluating continuously, tracing every request, and watching quality and cost as real inputs drift away from what you tested. That practice is LLMOps.
It also covers the decisions made before you build: prompting, RAG, or fine-tuning, and open or proprietary models. These are engineering trade-offs with real cost and maintenance consequences, not fashion choices. The articles here are how teams actually decided and operated.
Everything we have written on this
Cost Optimisation for LLM Workloads
From 2023–24 we saw AI bills spike - and then drop by 40–60% - when teams added basic observability, routing, and context hygiene.
LLM-as-a-Judge in Practice
LLM-as-a-judge emerged as a powerful evaluation tool in 2023–24 - and a dangerous one when used without calibration, guardrails, and human baselines.
LLMOps: What Was Production-Ready in 2024
Observability, evaluation, and cost tracking for LLMs matured fast between 2022–24. The hard part in 2024 was not tooling - it was picking a minimal, coherent stack.
Open vs Proprietary LLMs in 2024
The 2024 debate about open versus proprietary models was loud. In real enterprise projects, the decision came down to data sensitivity, latency, quality, and who would run the infra.
Prompting, RAG, or Fine-Tuning?
In theory, you could solve most problems with any combination of prompting, RAG, or fine-tuning. In practice, the constraints of 2022–24 pushed teams toward specific patterns.
Proof in production
Key terms
The service behind this
AI Integration and LLMOps
Connect, evaluate, and monitor your AI reliably.
