A practical checklist of systems an AI Evals & Reliability Engineer should build to measure, monitor, and harden LLM-based products.
Adapted from @suraj_sharma14As an AI Evals & Reliability Engineer, you must build these projects. Systems that prove you can measure, monitor & harden. 1.) LLM Regression Test Suite Build: pytest-style eval harness with golden datasets that blocks CI merges on quality drops. Why: If you can't measure quality in CI, you have a demo not a product. 2.) Trajectory Grading Engine Build: Step-level scoring of agent tool calls and reasoning paths, not just final answers. Why: Final-answer evals hide the exact step where the agent went wrong. 3.) RAG Retrieval Benchmark Build: Hit rate, MRR, NDCG tracking with adversarial queries that should return nothing. Why: Retrieval failures are silent. Metrics make them visible. 4.) LLM-as-a-Judge Calibration System Build: Judge prompts validated against human labels, with bias and agreement-rate reports. Why: An uncalibrated judge is just a second opinion you blindly trust. 5.) Hallucination Spike Monitor Build: Production alerting that flags missing citations and faithfulness drops in real time. Why: Quality drift kills trust before anyone reads the dashboard. 6.) Shadow Traffic Comparator Build: Route 5% of prod traffic to a new model or prompt, diff trajectories silently. Why: You cannot A/B test non-determinism without shadow mode. 7.) Chaos Test Suite for Agents Build: Inject API timeouts, malformed tool outputs, context overflow; measure recovery. Why: Agents fail in ways unit tests never predict. 8.) Cost and Latency Guardrail Middleware Build: Per-request token budgets, kill-switches, alerting on cost anomalies. Why: Reliability includes economics. A runaway loop is an outage. 9.) Golden Dataset Flywheel Build: Pipeline turning prod failures and thumbs-downs into versioned eval cases. Why: Stale evals pass while production burns. 10.) Fallback Chain Validator Build: Test frontier → mid-tier → local → cache degradation under simulated provider outages. Why: Your fallback path is your real product during an outage. 11.) Prompt and Config Regression Gate Build: Versioned prompts with automatic eval runs and rollback on any metric drop. Why: Prompt edits are deploys. Treat them like deploys. 12.) Distributed Tracing for LLM Hops Build: OpenTelemetry-style spans capturing prompts, tokens, latency, tool args per hop. Why: You cannot debug what you cannot replay. 13.) SLO and Error Budget Dashboard Build: Define AI SLOs (task success, faithfulness, TTFT) and track error budget burn. Why: Reliability engineering starts when you quantify "good enough." 14.) Injection and Jailbreak Fuzzer Build: Automated red-team suite fuzzing inputs and grading guardrail catch rates. Why: Safety is a reliability property, not a feature. 15.) Public Reliability Report Build: Published benchmarks, post-mortems, uptime and quality numbers for your shipped systems. Why: Public proof of reliability gets you hired faster than any resume. Most people watch tutorials. Builders ship systems. Bookmark & Repost.
