LLMOps Engineer
LLMOps engineers operationalise large language models in production, requiring sophisticated English to discuss retrieval-augmented generation pipelines, evaluation frameworks, and hallucination mitigation strategies with researchers, product managers, and executives. Their written communication spans evaluation reports, incident post-mortems for model regressions, and prompt versioning documentation. This path builds the advanced vocabulary and professional register needed to lead LLM deployments and communicate findings with clarity.
Topics covered
- RAG architecture & chunking
- LLM evaluation frameworks
- Prompt versioning & management
- Hallucination monitoring
- LLMOps tooling
- Latency & cost optimisation
Vocabulary spotlight
4 terms every LLMOps Engineer should know in English:
A technique that grounds LLM responses by retrieving relevant documents from an external knowledge base before generating a response
"Retrieval-augmented generation reduced hallucination rates on product knowledge queries by 60%."
A confident but factually incorrect or fabricated output produced by a language model
"We added a factual grounding check in the post-processing pipeline to catch hallucinations before they reach users."
Tracking and managing changes to prompts as versioned artefacts in the same way as application code
"Without prompt versioning, we had no way to reproduce the exact output from last month's experiment."
An automated framework for running a set of test cases against an LLM and scoring its outputs
"We built an evaluation harness that runs 500 golden questions after every model upgrade."
📚 Vocabulary Reference
Key terms organised by category for LLMOps Engineers:
RAG & Retrieval
LLM Evaluation
Prompt Engineering
Observability & Operations
Recommended exercises
Real-world scenarios you'll practise
- Writing an incident post-mortem after a hallucination event affected customer-facing output.
- Presenting RAG pipeline evaluation results to a product director, including precision and recall metrics.
- Explaining prompt versioning strategy to a software engineering team accustomed to code-only deployments.
- Drafting an LLM cost and latency optimisation proposal for the CTO.