LLMOps Training — LLM Application Operations, RAG, Guardrails & Evaluation

Who Should Attend

This program is for ML engineers, backend engineers, and platform engineers deploying LLM-powered applications to production. If your LLM prototype works in a notebook but breaks in production, prompts drift without detection, costs are unpredictable, or you have no way to evaluate output quality systematically — this course teaches LLMOps: the production discipline for LLM applications.

Learning Outcomes

• Implement prompt versioning and management — prompts as code, reviewed, tested, and deployed through CI/CD
• Operate RAG pipelines reliably — embedding freshness, retrieval quality monitoring, context relevance scoring
• Build LLM observability — token usage, latency, cost, and output quality tracking across every request
• Deploy guardrails for safety, PII detection, and jailbreak prevention — enforced at the API layer
• Implement evaluation pipelines — LLM-as-judge, reference-based, and human evaluation for continuous quality
• Optimize inference costs — model selection, caching strategies, batching, and routing

Course Modules

1. LLMOps Fundamentals — LLMOps vs. MLOps. LLM application architecture. Foundation models vs. fine-tuned models.
2. Prompt Management — Version-controlled prompts. Prompt templates. A/B testing prompts. Prompt performance dashboards.
3. RAG Pipeline Operations — Embedding pipeline reliability. Vector DB operations (Pinecone, Weaviate). Chunking strategies. Retrieval quality monitoring.
4. LLM Observability — LangSmith, LangFuse, Helicone. Token tracking. Cost per request. Latency monitoring. Output quality tracking.
5. Guardrails & Safety — NeMo Guardrails, Guardrails AI. Input validation. Output filtering. PII detection. Jailbreak prevention.
6. Evaluation Frameworks — RAGAS, DeepEval. LLM-as-judge. Groundedness, relevance, faithfulness, safety scoring. Regression testing for LLM outputs.
7. Inference Optimization — Model serving (vLLM, TGI). Caching. Batching. Cost-optimized model routing. GPU optimization.
8. CI/CD for LLM Applications — Testing prompts. Testing RAG pipelines. Canary deployment of prompt changes. Automated rollback.
9. LLM Governance — Audit trails. Compliance for LLM applications. Data residency. Model cards for foundation models.
10. Capstone: Production LLM Application — Build and deploy an LLM application with prompt management, RAG, observability, guardrails, and evaluation.

Hands-on Labs (20 total)

Labs include: “Set up prompt versioning in Git with automated evaluation on PR,” “Build a RAG pipeline with Pinecone and monitor retrieval quality with RAGAS,” “Configure NeMo Guardrails to block PII and jailbreak attempts,” “Implement LLM-as-judge evaluation that scores output groundedness and relevance.”

Frequently Asked Questions

How is this different from a prompt engineering course? Prompt engineering teaches you to write effective prompts. LLMOps teaches you to productionize them — version control, A/B testing, monitoring, guardrails, evaluation, cost optimization. This course assumes you can prompt; it teaches you to operate LLM applications at scale.

Which LLM providers do you cover? The course is provider-agnostic. Labs use OpenAI and open-source models (via vLLM/TGI), but the LLMOps principles apply equally to Anthropic, Google, Azure OpenAI, Cohere, or self-hosted models. We teach you to build operations that work across providers.