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.
What we would tell you on a call
Choose the model last
Pick the evaluation set, the latency budget and the cost ceiling first. Once those exist the model choice is usually obvious and, more importantly, revisitable: you can swap it when a better one ships instead of rebuilding around it.
Cost is a design decision, not a bill
Context length, retrieval breadth, caching and routing cheap requests to a smaller model are architectural choices made early. Discovering the bill after launch means changing the architecture under load.
Monitor output, not just uptime
A model endpoint can be perfectly healthy while the answers degrade. Sample real traffic, score it against the evaluation set, and alert on the score rather than only on the error rate.
Open or proprietary is a deployment question
It is decided by where the data is allowed to live, what latency you need and who carries the operational burden, far more often than by benchmark scores.
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
- LLMOps
- The operational practice of running language-model systems in production: versioning prompts and models, evaluating continuously, tracing requests, monitoring quality and cost, and managing rollout and rollback.
- Evaluation harness
- A repeatable test suite for an AI system: a fixed set of inputs, expected properties of a good answer, and scoring that runs automatically whenever anything changes.Without one, you cannot tell whether a prompt change or model upgrade improved the system or quietly broke it.
- LLM as a judge
- Using a language model to score another model's outputs against written criteria. Useful for grading qualities that are hard to check with exact matching, provided the judge is itself validated against human ratings.
- Cost per query
- The all-in token and infrastructure cost of answering one request. Tracking it per feature is what keeps an AI product economically viable at scale.
The service behind this
AI Integration and Operations
Connect, evaluate, and monitor your AI reliably.
