Phase 02
Build. The LLMOps stack is built before the model is deployed. Eval harness, monitoring, deployment pipeline, retraining cadence, all live before the first user query.
Methodology
LLMOps Lifecycle.
Operational discipline for LLM-based systems.
Category
MethodologyWhen we recommend it
Every Phase 02 generation- or augmentation-pattern engagement. Pure prediction-pattern engagements (classical ML) get classical MLOps; LLMOps applies when the system uses LLMs in production.
What it is
The framework, what it covers, and the problem it addresses.
The operational discipline for LLM-based systems in production. Five stages: data (curation, cleaning, retrieval design), evaluation (eval harness, regression suite, threshold gates), deployment (release management, rollback, canary), monitoring (drift detection, performance tracking, cost), retraining (cadence, triggers, regression validation). Modeled after MLOps but specialized for LLM-driven systems.
Why it matters
The reason this framework exists in the Rubix toolkit, and why omitting it is the wrong shortcut.
LLM systems fail differently than classical ML systems. They fail subtly (a quality regression invisible until a user notices), they fail expensively (cost can spike with prompt changes), and they fail at the edge (rare inputs produce hallucinations). LLMOps is the discipline that catches these failures before customers do. Without it, you have a demo that worked once.
In the Kingdom and the GCC
Regional context. PDPL, SDAIA, Vision 2030, Saudization, and the operating realities that shape how this framework lands here.
In KSA, LLMOps takes on additional dimensions: bilingual evaluation (AR/EN equivalence checking), sovereign deployment (KSA-resident inference for sensitive use cases), and PDPL-compliant logging (production logs cannot leak personal data). These constraints shape the LLMOps stack.
How Rubix applies it
The phases of the Rubix Way where this framework is operationalized, and what we do with it there.
Phase 03
Scale. LLMOps becomes the daily discipline. Drift detection runs continuously. Retraining triggers are documented. Audit trails are queryable.
Common pitfalls
The failure modes we have seen up close, written so the next engagement avoids them.
- 01
Treating LLMOps as a Phase 03 concern. By Phase 03, the eval debt is overwhelming. LLMOps is built in Phase 02 from the first sprint.
- 02
Borrowing MLOps tooling unchanged. MLOps tools assume static models. LLMOps tools must handle prompt changes as a release event.
- 03
Evaluating only at deployment. The eval suite must run continuously in production, not just at release.
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