Course Objective & Scope
An LLM application that shines in a demo often runs into unexpected hallucinations, silent quality drift, high latency and unpredictable API bills once real users arrive. This foundational programme explains why classic DevOps and MLOps practices alone are not enough and introduces the core building blocks of the LLMOps lifecycle in a practical, approachable format.
Working on a sample chat and RAG application, participants wire up a prompt registry, basic OpenTelemetry/Langfuse tracing, a starter eval dataset and baseline guardrails, leaving with an initial production quality gate checklist for their organisation.
Who Should Attend?
- Software developers preparing their first LLM application for production
- DevOps and platform engineers moving from classic software or MLOps into GenAI projects
- Technical product managers and team leads who need to understand production AI risks
- Prerequisite: basic Python and REST API familiarity
Key Takeaways
- Explain the operational challenges posed by non-deterministic LLM outputs
- Manage prompts and model configurations separately from application code
- Record latency, token counts, cost and intermediate steps as traces on every LLM call
- Run a baseline eval dataset and verification check before release
- Prepare a production readiness quality gate checklist for internal LLM projects
Curriculum
From DevOps and MLOps to LLMOps
- Why LLM applications cannot be validated with traditional unit tests alone
- The LLMOps lifecycle: build, evaluate, deploy, observe and iterate
Prompt, Model and Configuration Management
- Risks of hard-coded prompts and moving to centralised prompt templates
- Managing model providers, temperature and tool schemas across environments
Observability and Recording Your First Traces
- Traces and spans: gaining visibility across RAG and tool calls
- Monitoring token usage, latency and error rates in one dashboard
Baseline Evaluation and Guardrails
- Testing changes against a 15–20 example starter eval dataset
- PII masking, output format checks and fallback rules
The Production Quality Gate
- Pre-launch checklist for security, cost, latency and quality
- Fitting LLMOps steps into the organisation's existing delivery workflow
Other Courses in This Category
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Bringing full production discipline to an LLM application: LiteLLM gateway, Langfuse/Phoenix tracing, automated eval and regression tests, CI/CD quality gates, runtime guardrails and live user feedback loops.
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Full reproducibility in LLM applications: prompt versioning with Langfuse/MLflow Prompt Registry and Git, model/LoRA and tool schema versioning, RAG/eval dataset snapshots and safe rollbacks.
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Consolidating cloud and self-hosted LLMs behind a single OpenAI-compatible gateway with LiteLLM and enterprise AI gateways: automated fallbacks, load balancing, complexity-based routing, virtual keys and budget/rate-limit governance.
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GitHub Actions and GitLab CI pipelines for LLM and RAG applications: fast PR smoke and eval suites, deterministic and model-graded quality gates, cost and latency budget checks, canary rollouts and automated rollbacks.
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Systematic quality measurement for LLM applications: deterministic and LLM-as-a-judge metrics with Promptfoo, DeepEval and Ragas, mitigating judge biases, calibrating against human labels and building an enterprise eval scorecard.
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Engineering fast, repeatable, large-scale LLM evaluation pipelines: async parallel test runners, judge cost and cache optimisation, dataset ingestion, statistical significance and experiment comparison dashboards.
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