MLOps pipelines built for traditional ML models do not address the unique operational requirements of large language models. LLMs are not retrained on every release. Their behavior is controlled primarily through prompts, retrieval configurations, and orchestration logic rather than model weights. An LLMOps pipeline manages the full lifecycle of these LLM-specific artifacts.

Pipeline Stages

Prompt development - Authors write and iterate on system prompts, few-shot examples, and output schemas in a version-controlled repository. Each prompt version is tagged and linked to evaluation results. Changes go through code review just like application code.

Evaluation - Automated evaluation runs test each prompt version against a curated dataset of inputs and expected outputs. Evaluation metrics include task-specific accuracy, latency, cost per request, and safety checks. Use both automated metrics (BLEU, ROUGE, exact match) and LLM-as-judge approaches for subjective quality assessment.

Integration testing - Test the full chain: retrieval, prompt assembly, model call, output parsing, and guardrail filtering. Mock external dependencies to isolate the AI pipeline. Verify that the system handles edge cases such as empty retrieval results, oversized context windows, and malformed model responses.

Staging deployment - Deploy the new configuration to a staging environment that mirrors production traffic patterns. Run shadow traffic or a percentage-based canary to compare the new version against the current production baseline.

Production promotion - Promote the new version based on evaluation gates: latency within SLA, cost within budget, quality metrics above threshold, and no safety regressions. Use feature flags to enable gradual rollout and instant rollback.

Monitoring - Track production metrics continuously: response latency, token usage, cost per request, error rates, guardrail trigger rates, and user feedback signals. Alert on regressions in any metric.

Key Differences from Traditional MLOps

Traditional MLOps pipelines center on model training, validation, and deployment. LLMOps pipelines center on prompt configuration, evaluation, and orchestration. The model itself is often a third-party API that does not change between releases. What changes is the system around the model.

This means the pipeline must version and evaluate prompt templates, retrieval configurations, tool definitions, and guardrail rules as first-class artifacts. Each combination of these artifacts constitutes a release candidate.

Cost Management

LLM API costs scale with token volume. The pipeline must track cost per evaluation run and enforce budgets. Use caching layers to avoid redundant API calls during evaluation. Set per-environment spending limits and alert when costs approach thresholds.

Tooling

Build the pipeline on standard CI/CD infrastructure (GitHub Actions, GitLab CI, Jenkins) with LLM-specific evaluation steps. Use evaluation frameworks like promptfoo, DeepEval, or custom harnesses that support your specific metrics. Store evaluation results alongside prompt versions for traceability.