LLM reliability best practices checklist

Last month I watched our deployed assistant start inventing team rules. It told a PM they could no longer schedule meetings on Fridays, and it gave a list of "approved" Slack emojis. I spent an emergency hour rolling back the model and two more hours in a postmortem. We had missed a few basic checks. I still laugh about it, and then I remember we almost made mandatory no-Friday-meetings real.

I have worked on AI features at Zomato and BrowserStack. I write this checklist from that experience. The focus is practical. The primary keyword is LLM reliability best practices. I will cover versioning, testing, monitoring, drift detection, and human review. I will use everyday analogies like cooking and flight recorders. The goal is a checklist you can follow. It will help with AI pipeline reliability, LLM MLOps best practices, and building robust LLM pipelines.

1. Set clear reliability goals and SLOs

• Define concrete SLOs for latency, availability, and quality. For example, 95th percentile latency less than 300ms, availability 99.9 percent, and hallucination rate less than 1 percent.

• Specify measurable quality metrics. Use exact match, BLEU, F1, safety violation rate, and task success rate for core use cases.

• Document acceptance thresholds, who owns each SLO, and store them in a versioned policy file for audits.

Analogy: Think of SLOs like a service level menu that the whole team agrees on before cooking starts. The chef needs to know what dishes will be served and how fast. The rest of the team needs to agree on which dishes are acceptable.

Questions answered: How do I make LLMs reliable in production? What are best practices for LLM MLOps?

Link: See the LLMOps & Production AI pillar page for templates and deployment patterns.

2. Version everything, not just the model

• Version model checkpoints, tokenizer, prompt templates, evaluation suites, and preprocessing code with commit hashes or tags.

• Record semantic model metadata. Include model name, weights hash, training data snapshot, fine-tune recipe, and inference config.

• Keep prompt and prompt-schema versions in the same repository as code. Require a changelog entry for prompt updates.

Analogy: Treat prompts and tokenizers like database schema changes. They need migrations and a record. If you change the schema without a migration, old data breaks.

Questions answered: How to version LLM models and prompts? What are best practices for LLM MLOps?

Link: Check the LLMOps & Production AI pillar page for example versioning policies.

3. Build deterministic test suites and unit checks

• Create fixed, representative test cases that exercise expected behavior and edge cases. Store them in a test corpus with IDs.

• Add unit tests for preprocessing, postprocessing, and prompt assembly. These must pass in CI before deployment.

• Make tests verifiable. For example, assert that sample output contains required fields or matches one of the approved responses.

Analogy: Like automotive safety checks, run the same brake test before every rollout. If the brake test fails, you do not drive the car.

Questions answered: How do I make LLMs reliable in production? What is continuous evaluation for LLMs?

Link: See the AI Debugging & Reliability pillar page for test design patterns.

4. Run continuous evaluation and leaderboards

• Automate daily or per-PR evaluations on a held-out benchmark suite. Cover accuracy, safety, and user flows.

• Keep a continuous leaderboard that logs model versions, prompt versions, metric deltas, and evaluation timestamps.

• Fail deployments automatically if a new candidate performs worse than baseline on any critical metric beyond a margin you define.

Analogy: Think of it as continuous QA and a scoreboard that must be beaten to move to production. You do not promote a player without seeing the scorecard.

Questions answered: What is continuous evaluation for LLMs? How do you detect LLM drift?

Link: See the AI Debugging & Reliability pillar page for leaderboard examples.

5. Implement canary and staged rollouts

• Deploy to a small percentage of real traffic first. Monitor key signals for a defined observation window. Then expand.

• Use automated rollbacks on metric regression. Require manual approval for any increase in hallucination or safety flags.

• Record canary experiments with the exact model, prompt, and routing rules so the test is reproducible.

Analogy: Like releasing a new recipe to a few customers before changing the whole menu. If a sample dislikes it, you stop and fix the recipe.

Questions answered: How do I make LLMs reliable in production? How to set up alerts for LLM failures?

Link: Refer to the LLMOps & Production AI pillar page for staged rollout guides.

6. Monitor LLM specific metrics and set alerts

• Track latency percentiles, error rates, throughput, cost per request, and resource saturation as standard infra metrics.

• Add LLM specific metrics: hallucination rate, refusal rate, answer confidence proxy, token usage, and semantic drift scores.

• Define alert thresholds and actionable playbooks. For example, alert if hallucination rate increases by 50 percent for 30 minutes.

Analogy: Like a car dashboard showing oil, temperature, and check engine light. Specific gauges help you act before the engine fails.

Questions answered: What metrics should I monitor for LLMs? How to set up alerts for LLM failures?

Link: See the LLMOps & Production AI pillar page for alerting templates.

7. Detect drift and data distribution shifts

• Measure input distribution drift with feature statistics, embedding distance, or KL divergence between historical and recent traffic.

• Monitor output drift by comparing recent outputs against baseline embeddings and by tracking metric degradation on rolling window evaluations.

• Trigger retrain, data refresh, or prompt updates when drift crosses predefined thresholds and validate changes on a holdout set.

Analogy: Like noticing your spice supplier changed, it affects the taste and needs correction. If curry suddenly tastes different, you check the spices.

Questions answered: How do you detect LLM drift? How do I make LLMs reliable in production?

8. Record input, output, and lineage for observability

• Log inputs, prompts, model version, deterministic seeds, and outputs, while respecting privacy and retention rules.

• Implement tracing that ties a user request to pipeline components and prompts used so failures can be reproduced.

• Provide quick queries to fetch samples that tripped safety filters or caused bad user outcomes for postmortems.

Analogy: Like a flight recorder, keep enough context to reconstruct what happened. You need the black box when things go wrong.

Questions answered: How do I make LLMs reliable in production? What metrics should I monitor for LLMs?

Link: See the AI Debugging & Reliability pillar page for tracing patterns.

9. Create an LLM validation and verification layer

• Add a validation stage that checks LLM outputs for format, hallucination indicators, safety policy violations, and factuality signals.

• Use lightweight validators and a fallback plan. For example, call a verifier model, apply rule checks, or route to human review.

• Record verifier decisions and false positive rates so validators themselves are continuously evaluated.

Analogy: Like a quality inspector on an assembly line before items ship. The inspector catches defects before customers see them.

Questions answered: How to handle hallucinations in LLM outputs? Best practices for building a robust LLM validation layer?

Link: See the AI Debugging & Reliability pillar page for validator implementations.

10. Human-in-the-loop and feedback collection

• Define clear sampling rules for human review of outputs. For example, review 1 percent of production responses and all safety flags.

• Instrument user feedback buttons and link feedback to the original inputs and model version for retraining signals.

• Track reviewer agreement rates and use disagreement as a signal for ambiguous prompts or task definition issues.

Analogy: Like having senior chefs taste random orders to catch subtle problems. Their feedback helps maintain quality.

Questions answered: How do I make LLMs reliable in production? What is continuous evaluation for LLMs?

11. Plan capacity, cost, and autoscaling for LLM loads

• Perform load tests that use realistic prompt sizes and token generation patterns to surface tail latency and cost behavior.

• Set autoscaling policies based on request concurrency and model token generation time, with safe headroom to prevent queueing.

• Measure cost per successful request and run cost impact analysis for candidate models before rollouts.

Analogy: Like staffing a restaurant for peak hours based on real reservation patterns. Understaffed shifts lead to long waits.

Questions answered: What are best practices for LLM MLOps? How do I make LLMs reliable in production?

Link: See the LLMOps & Production AI pillar page for capacity planning templates.

12. Prepare runbooks and incident response for LLM failures

• Create runbooks for common failure modes: hallucination spikes, latency regressions, availability loss, and safety breaches.

• Include diagnosis steps, immediate mitigations such as rollback or throttling, and postmortem checklist items.

• Set RTO and RPO targets for LLM services and rehearse incident drills periodically.

Analogy: Like a fire drill for systems. People must know exactly what to do under pressure.

Questions answered: How to set up alerts for LLM failures? How do I make LLMs reliable in production?

Link: See the LLMOps & Production AI pillar page for incident playbooks.

13. Enforce safety, privacy, and compliance checks

• Define data retention, logging scope, and redaction rules for prompts and outputs that contain PII.

• Add privacy-safe sampling and synthetic test flows to validate behavior without exposing real user data.

• Keep audit trails for model decisions, and document compliance controls for regulators or internal audits.

Analogy: Like sealing confidential documents in a safe. Only authorized staff can access them.

Questions answered: How do I make LLMs reliable in production? What metrics should I monitor for LLMs?

14. Governance, access control, and change approval

• Require change reviews and approvals for model, prompt, and evaluation changes, with approvals recorded in an audit log.

• Use role-based access control for who can deploy models, update prompts, or change evaluation thresholds.

• Add automated policy checks in CI that block changes that remove safety checks or increase exposure risk.

Analogy: Like a bank vault with multiple keys and approvals for big moves. No one person can move everything alone.

Questions answered: What are best practices for LLM MLOps? How to version LLM models and prompts?

Link: See the LLMOps & Production AI pillar page for governance templates.

15. Benchmark, baseline, and continuous improvement

• Maintain baselines and track performance deltas over time. Require any improvement to be validated across multiple metrics.

• Include adversarial and stress benchmarks to catch brittleness not visible in standard tests.

• Use the leaderboard history to choose the safest candidate, not just the highest average score.

Analogy: Like keeping old test scores to see if new training actually improves the student. Old scores give context.

Questions answered: What is continuous evaluation for LLMs? Best practices for building a robust LLM validation layer?

Link: See the AI Debugging & Reliability pillar page for benchmarking strategies.

16. Apply LaikaTest inspired practices for LLM workloads

• Integrate continuous adversarial and safety tests, modeled after LaikaTest practices, to stress models on real-world failure modes.

• Use LaikaTest style automated continuous evals and alerting patterns to detect regressions quickly and block unsafe rollouts.

• Version validation suites and keep a history of failed adversarial cases so fixes are reproducible and measurable.

Analogy: Like adding a special quality inspector who tries to break the product every day. You learn the weak spots fast.

Questions answered: What is continuous evaluation for LLMs? How to handle hallucinations in LLM outputs?

LaikaTest is an AI infrastructure tool for production LLM systems that helps teams experiment, evaluate, and debug prompts and agents safely in real usage. It addresses common problems. Teams change prompts or agent logic but do not know if behavior actually improved. AI outputs are non-deterministic, so "it felt better" is not evidence. Observability tools show logs, but do not tell which version performed better. Silent regressions happen after prompt or model changes.

LaikaTest enables prompt A/B testing. It lets you run multiple prompt variants on real traffic and compare outcomes. It supports agent experimentation. It provides one line observability and tracing. You can see which prompt version was used, model outputs, tool calls, costs, and latency. It also adds an evaluation feedback loop. You can collect human or automated scores tied to the exact prompt version.

I recommend adopting these checklist items as part of your LLM pipeline. Start small by adding versioning, deterministic tests, and continuous evaluations. Then add canaries, monitoring, and human review. Use LaikaTest inspired adversarial suites and automated alerts to catch LLM specific failures early. For debugging patterns, see the AI Debugging & Reliability pillar page. For deployment and governance templates, see the LLMOps & Production AI pillar page.

Follow this checklist, and you will reduce surprises. You will also build better, safer, and more reliable LLM systems.