LLMOps — Interview Questions Booklet (50 Q&A)
Comprehensive answers • Deployment & Monitoring • Guardrails & Eval • Cost & Compliance • Real-world Scenarios
1) What is LLMOps?
Answer: LLMOps (Large Language Model Operations) is the discipline of reliably running LLM-powered systems in production. It combines DevOps/MLOps practices with LLM-specific needs: prompt/version management, retrieval pipelines, guardrails, evaluation, monitoring, cost control, and compliance. The goal is safe, predictable, auditable outcomes at scale.
2) Why is LLMOps important?
Answer: LLMs are probabilistic, expensive, and prone to hallucinations. Without operational rigor, orgs face runaway costs, safety incidents, and inconsistent UX. LLMOps introduces controls, measurement, and feedback loops so teams can iterate fast while meeting SLAs, governance, and budget targets.
3) How does LLMOps differ from MLOps?
Answer: MLOps centers on training/serving classical models. LLMOps focuses more on inference orchestration, prompt lifecycle, RAG, few-/fine-tuning, evaluation of unstructured text, and safety. Cost/latency per request, context limits, and content risks are first-class concerns.
4) What are the core building blocks of an LLM system?
Answer: Request router, prompt manager, retriever/index, model gateway(s), safety filters/guardrails, evaluators, observability stack, and cost controls. Enterprise systems add policy engines, AB testing, canaries, and rollbacks—treated as code with CI/CD.
5) What lifecycle does LLMOps manage?
Answer: Ideation → dataset creation → prompt/graph design → eval harness → canary → production → monitoring → feedback & iteration. It also covers data governance, model routing, cost/latency tuning, incident response, and deprecation.
6) Self-host vs. API providers: how to choose?
Answer: APIs offer speed-to-value and managed reliability; self-hosting gives data control, potential cost leverage, and on-prem options. Consider privacy, latency, throughput, flexibility, and TCO (hardware, ops skills). Many enterprises run hybrid: API for complex tasks, local for sensitive/routine ones.
7) What are common inference serving patterns?
Answer: Managed endpoints (serverless), containerized model servers (vLLM/TensorRT-LLM), and multi-tenant gateways behind an API. Use autoscaling with queue depth, dynamic batching, and warm pools to minimize cold starts. Pin heavy routes to dedicated capacity for predictability.
8) How do quantization and distillation help?
Answer: Quantization (e.g., 8/4-bit) reduces memory and speeds inference with minimal quality loss if calibrated. Distillation transfers knowledge from a large model to a smaller student, improving latency and cost. Both are core levers for achieving production economics.
9) What’s model routing and when is it useful?
Answer: Routing picks the cheapest/fastest model that meets task quality, using heuristics or learned policies. Simple classification might use a small model; legal analyses route to a strong model with verification. Routing curbs spend and reduces tail latency.
10) How do you integrate LLMOps with CI/CD?
Answer: Treat prompts, retrieval configs, evaluation suites, and routing graphs as code in repos. Validate via unit/eval tests in CI. Use progressive delivery (canary/AB) and capture full traces to compare versions before global rollout.
11) What is prompt management?
Answer: A system to version, test, and deploy prompts (and tool/graph instructions) with metadata and rollback. Include templates, variables, examples, and JSON schemas. Track performance by prompt version across cohorts and tasks.
12) How to make prompts reproducible?
Answer: Fix model version/params, freeze retrieval seeds, and log full context windows. Use deterministic tools and structured outputs. Store prompt+inputs+outputs in a trace store to replay and compare.
13) Strategies for controlling output format?
Answer: Specify JSON schemas with explicit keys, types, and examples. Validate output and retry with error messages when parsing fails. Where supported, use tool/function calling for stricter contracts.
14) How to manage long contexts?
Answer: Summarize earlier turns, compress with embeddings, and fetch only relevant chunks via RAG. Prefer structured state over dumping raw history. For very long tasks, persist artifacts and use stepwise planners.
15) What’s few-shot vs. fine-tuning vs. adapters?
Answer: Few-shot provides examples in the prompt; fast to iterate but costs tokens. Fine-tuning adapts weights for task style/format; better consistency but requires data and governance. Adapters/LoRA offer lightweight tuning that’s cheaper to train and deploy.
16) What is RAG and why use it?
Answer: Retrieval-Augmented Generation fetches relevant facts from external sources to ground model outputs. It boosts factuality, allows fresh knowledge without full retraining, and supports citations. It’s central to enterprise truthfulness.
17) How do you design a good indexing pipeline?
Answer: Clean and normalize content, chunk with task-aware heuristics, enrich with metadata (source, date, permissions), and compute embeddings with a stable model. Schedule refresh jobs, and maintain lineage so you can trace answers to sources.
18) Hybrid retrieval vs. vector-only?
Answer: Hybrid combines keyword (BM25) and dense vectors, improving recall on exact terms and semantics. It’s robust to rare words, acronyms, and noisy text. Use rerankers to refine top-k into top-n high-quality contexts.
19) How to prevent stale or irrelevant retrieval?
Answer: Add freshness filters, source whitelists, and semantic reranking. Monitor retrieved-to-used ratios and citation coverage in final answers. Retrain embeddings or re-chunk when drift is detected.
20) How do permissions and personalization work with RAG?
Answer: Enforce row/field-level security at retrieval time; include only docs a user can access. Personalize ranking using user profile and past interactions. Log access decisions for audit.
21) How do you evaluate LLM outputs?
Answer: Use golden datasets, reference-based metrics, and rubric-based evaluators. Track factuality, consistency, toxicity/bias, adherence to format, latency, and cost. Blend automatic checks with human review for critical tasks.
22) Offline vs. online evaluation?
Answer: Offline evals are fast, repeatable, and safe; they gate releases. Online (canary/AB) captures real-world behavior, drift, and user satisfaction. Use both: offline to qualify, online to verify.
23) What is a rubric-based evaluator?
Answer: A checker that scores outputs against explicit criteria (e.g., cites 2+ sources; no PII; JSON valid). It can be rule-based or model-based. Rubrics enable transparent, auditable acceptance tests.
24) How to design a regression test suite?
Answer: Curate diverse tasks, edge cases, and adversarial inputs. Record expected behaviors or thresholds. Fail builds when metrics regress; store traces to pinpoint deltas by prompt/model/route.
25) What’s hallucination testing?
Answer: Evaluate whether outputs invent facts or misattribute sources. Use questions with known answers and contrastive negatives. Penalize unsupported claims and require citations or abstentions.
26) What are guardrails?
Answer: Pre/post-processing checks and policies that constrain inputs/outputs and tool actions. Examples: allowed tools/domains, PII redaction, jailbreak detection, safe-complete templates. They reduce risk without retraining.
27) How do you prevent prompt injection?
Answer: Isolate user content from system prompts, strip/neutralize control tokens, and sanitize retrieved text. Use allowlists for tool calls and refuse instructions that conflict with system policy. Log suspected attacks for tuning.
28) How to handle PII and sensitive data?
Answer: Detect and redact PII before inference; minimize retention; encrypt at rest/in transit; restrict who can view traces. Provide user deletion and data residency controls. Prefer local inference for regulated data.
29) What’s the role of a policy engine?
Answer: A policy engine enforces organization rules (content, cost, privacy) at request time. It decides whether to route, block, or require human approval. It centralizes governance while letting teams iterate quickly.
30) When to involve a human-in-the-loop (HITL)?
Answer: Trigger HITL on low confidence, high-risk actions, policy violations, or budget overruns. Provide reviewers with full traces and one-click approve/deny. Capture outcomes to refine policies and prompts.
31) What should you monitor?
Answer: Latency, tokens, cost, error rates, output format validity, safety flags, hallucination incidents, cache hit rate, retrieval freshness, and user feedback. Correlate with model/prompt/version and route.
32) What does a good trace include?
Answer: Prompt template + variables, full context (redacted), model/params, tool calls with inputs/outputs, retrieval docs, safety decisions, and final output. Traces enable root-cause analysis and audits.
33) How to detect quality drift?
Answer: Watch success rates, rubric scores, and complaint types over time. Compare canary vs. control cohorts. Correlate anomalies with prompt/model changes or index refreshes.
34) How do you alert meaningfully?
Answer: Set SLOs for latency, cost/task, format validity, and safety incidents. Page on sustained SLO breaches, not single spikes. Include context and links to traces and rollback buttons.
35) How to design dashboards?
Answer: Provide overviews (traffic, latency, cost), quality (success, hallucination rate), safety (blocked/flagged), and infra (GPU utilization, cache hits). Add filters by route/model/prompt and drillthrough to traces.
36) Top levers for cost reduction?
Answer: Model routing, prompt compression, context slimming with better retrieval, semantic/tool-result caching, and early exits. For self-host, add quantization, distillation, and right-sizing hardware.
37) How to optimize latency?
Answer: Parallelize independent calls, stream partials, reduce context size, and keep warm pools. Use high-throughput inference engines and batch where acceptable. Avoid unnecessary retries via better validation.
38) What cost KPIs matter?
Answer: Cost per successful task, token efficiency (useful output per token), cache hit rate, cost per user, and cost per route. Track by environment and cohort to spot regressions early.
39) How to use caching safely?
Answer: Scope caches to prompts+variables+policies; include model/prompt versions in keys. For semantic caches, set similarity thresholds and TTLs. Always log cache provenance with the response.
40) Batch vs. streaming trade-offs?
Answer: Batching raises throughput/efficiency but adds queueing delay; great for bulk jobs. Streaming improves perceived latency and UX for long responses but may increase token costs. Choose per route and user need.
41) What compliance concerns are unique to LLMs?
Answer: Data residency, PII handling in prompts/traces, content safety, copyright/citation, and vendor usage logs. Provide subject access/deletion, consent tracking, and clear retention policies.
42) How to audit an LLM system?
Answer: Preserve immutable, redacted traces with versioned artifacts (prompt/model/index). Record policy decisions and reviewer actions. Produce evidence packs (evals, canaries, incident reports) on demand.
43) How to enforce data minimization?
Answer: Collect only necessary fields, prefer derived signals to raw data, and drop prompts after post-processing when feasible. Apply TTLs and anonymization; mask secrets end-to-end.
44) Third-party model/vendor risk management?
Answer: Maintain an approved vendor list, review SLAs and security posture, and monitor for outages or policy changes. Abstract your gateway so you can hot-swap vendors without app rewrites.
45) What belongs in an LLM policy?
Answer: Allowed use cases, data classes and handling rules, logging/redaction standards, safety thresholds, review/approval workflows, incident response, and model selection criteria. Make it living and testable.
46) You see cost spikes after a release. What do you do?
Answer: Roll back via version pin or reduce canary traffic. Inspect traces for context bloat or route changes, verify cache keys, and compare eval deltas. Add token caps and prompt compression before re-ramping.
47) Outputs started failing JSON validation in prod.
Answer: Pin model/params, retry with structured error messages, and tighten schema examples. If still failing, route through function-calling or a smaller, more reliable model for formatting. Add contract tests to CI.
48) Retrieval quality regressed after reindexing.
Answer: Check chunking parameters, embedding model drift, and metadata filters. Compare top-k overlap with prior index; run reranker diagnostics. Roll back index or switch to hybrid retrieval until fixed.
49) A jailbreak succeeds in a live route.
Answer: Triage and block the pattern, add input sanitizers and stronger system prompts, and tighten tool allowlists. Re-run attack suites pre-release; require HITL for high-risk actions until metrics recover.
50) How do you present LLMOps success to leadership?
Answer: Show business KPIs (task success, CSAT), reliability (SLO adherence), safety (incident rate < threshold), and efficiency (cost/task down, cache hit up). Share trace-backed case studies and the roadmap for further savings and risk reduction.