Essential LLM QA checklist every product manager should use

Introduction

Integrating large language models (LLMs) into digital products demands a rigorous quality assurance (QA) approach. For a product manager (PM), it is crucial to ensure that AI features are not only performant but also reliable, ethical, and secure in production. Evaluation and monitoring are complementary, continuous phases that guarantee the quality and robustness of AI systems throughout their lifecycle. This LLM QA checklist, based on best practices and evaluation principles, provides a structured framework to guide PMs in this process.

1. Evaluation of model capabilities and performance

Before deployment, the PM must ensure the LLM has been thoroughly tested on its core capabilities.

• Accuracy and consistency: Does the model produce correct, logical answers without contradictions?
• Context understanding: Does it grasp nuances, implicit references, and the overall meaning of user queries?
• Generation quality: For text generation tasks, is the output fluent, grammatically correct, stylistically appropriate, and relevant?
• Robustness and resilience: Does the model perform well even with unusual, noisy, or ambiguous inputs, without amplifying biases?
• Factual knowledge: Has it been tested for hallucination, i.e., inventing facts?
• Computational performance: Are latency and capacity to handle request volumes acceptable?

2. Evaluation of alignment and ethics

A responsibility imperative ensuring the model respects human and societal values.

• Bias detection and correction: Does the model avoid perpetuating prejudices and discriminatory content? Are corrective measures in place?
• Appropriate content and moderation: Does it avoid toxic, hateful, violent, or private information disclosure? Has it been tested on sensitive queries?
• Truthfulness: Can it distinguish true from false information and refuse to provide harmful or incorrect content?
• Adherence to values: Does it follow desired ethical guidelines (politeness, absence of stereotypes, etc.)?

3. Security evaluation

Security is critical to mitigate risks from malicious uses.

• Resistance to prompt attacks: Can the model withstand attempts to bypass safety filters?
• Reasoning reliability: Does it avoid subtle or dangerous errors, especially in sensitive contexts (e.g., medical)?
• Confidentiality: Does it refrain from revealing sensitive training data?

4. Own your prompts and prompt engineering

Mastering prompts is a key quality factor, aligned with the “12-Factor Agents” principle.

• Structured prompt management: Does the team have a clear approach to manage, version, and test prompts?
• Quality beyond 80%: Is the PM aware that common frameworks reach only 70-80% quality, and exceeding this requires reverse engineering prompts, control flow, and sometimes a full redesign? This is a costly and critical step.

5. Continuous monitoring in production

Post-deployment monitoring ensures long-term model reliability and relevance.

• Drift detection (data/concept drift): Is there a system to detect changes in input data or model behavior over time that could degrade performance?
• Real-time performance metrics: Are technical KPIs (accuracy, precision, recall) tracked in production, even without immediate ground truth?
• Business and usage indicators: Are business metrics (conversion rates, clicks) and user satisfaction indicators monitored to reflect real model value and UX impact?
• Trust and ethical metrics: Are sensitive response rates, toxicity scores, and fairness indicators tracked in production?
• User feedback loop: Is there a mechanism to collect explicit user feedback (surveys, ratings) and analyze implicit behavior (frequent query reformulations)?
• Alerts and automated re-evaluation: Are alert thresholds set for critical metrics, triggering automatic warnings? Is there a retraining or recalibration plan if performance degrades?

6. Holistic approach and transparency

The goal is a comprehensive view of performance—business, ethical, and technical.

• Documentation and traceability: Is the history of metrics and model changes kept for trend analysis and decision justification?
• Continuous integration (CI/CE/CD): Are model tests continuous and integrated into the development pipeline, reinforcing that evaluation is iterative throughout the LLM product lifecycle?

Conclusion

Incorporating a comprehensive QA checklist tailored to LLMs is essential for product managers aiming to deliver reliable, ethical, and secure AI-powered products. Prompt testing and continuous monitoring are not optional steps but fundamental pillars that prevent costly failures, mitigate risks, and build user trust. By embracing a structured approach, spanning technical evaluation, ethical alignment, security assurance, and ongoing performance tracking, PMs can navigate the complex challenges of LLM deployment. This ensures their products maintain high-quality standards over time, adapt to evolving data and user behaviors, and ultimately provide consistent value and safety in production environments.