LLMOps Interview Questions and Answers

Description

• LLMOps
  1. Definition: LLMOps is the discipline for operationalizing large language models across their lifecycle—covering deployment, tuning, monitoring, and governance.
  2. Scope: It focuses on prompt engineering, model selection, fine‑tuning, retrieval integration, cost control, and safety for foundation models used in production.
  3. Data practices: Emphasizes curation of high‑quality corpora, dataset versioning, and provenance so training and evaluation data are auditable and reproducible.
  4. Prompt management: Uses prompt/version stores, templates, and A/B testing to treat prompts as first‑class artifacts that evolve with the application.
  5. Model lifecycle: Tracks model versions, checkpoints, and metadata in a registry; supports promotion workflows from dev to staging to production.
  6. Fine‑tuning strategies: Supports instruction tuning, supervised fine‑tuning, and continual learning pipelines to adapt base models to domain tasks.
  7. Parameter‑efficient tuning: Implements LoRA, adapters, and low‑rank methods to fine‑tune large models cost‑effectively without full retraining.
  8. Retrieval Augmented Generation: Integrates vector stores, indexing, retrieval strategies, and context assembly (RAG) to ground outputs in external knowledge and reduce hallucinations.
  9. Monitoring and observability: Monitors latency, token usage, hallucination rates, retrieval hit rates, user satisfaction, and drift, with dashboards and alerting for regressions.
  • Safety and alignment: Enforces content filters, safety classifiers, policy gates, and human review workflows to mitigate harmful or biased outputs.
  • Testing and validation: Uses scenario tests, adversarial prompt tests, and benchmark suites to validate behavior across edge cases and slices.
  • Cost and performance optimization: Applies model selection, quantization, distillation, batching, and caching to meet latency SLAs while controlling token and compute costs.
  • Scalability patterns: Employs sharding, model parallelism, hybrid CPU/GPU inference, and autoscaling for high‑throughput production workloads.
  • Closed‑loop feedback: Captures user feedback and telemetry to retrain, refine prompts, or update retrieval indices in automated or semi‑automated loops.
  • Explainability and auditing: Records prompt history, context used, and provenance of retrieved documents to enable audits and explain model outputs.
  • Security and governance: Enforces RBAC, secrets management, encrypted stores, and compliance logging for sensitive deployments.
  • Tooling ecosystem: Combines model registries, prompt stores, vector databases, orchestration pipelines, and cost dashboards to operationalize LLMs reliably.
  • Adoption path: Start with prompt/version control and RAG for grounding, add monitoring and safety layers, then introduce fine‑tuning and closed‑loop automation as confidence and scale grow.