Best Practices and Limitations

Product professionals face ethical dilemmas daily when deciding what information to include in prompts. The convenience of AI analysis must balance with responsibility to users and company data. Consider what happens when you paste user interviews or support tickets into ChatGPT. Names, email addresses, and personal stories become part of your prompt. While ChatGPT doesn't permanently store conversations in most cases, the practice still raises ethical concerns about consent and data handling.

Develop habits that protect information while maximizing ChatGPT's usefulness. Anonymize data before sharing, use placeholder names, and remove identifying details. When analyzing competitive products, avoid sharing proprietary information that could compromise your company's position.

ChatGPT can generate statistics that sound perfectly legitimate. For example, it could say "99% of users abandon carts due to complex checkout processes." It’s compelling data for your next product meeting; except it might be completely invented. Product decisions based on such fictional data lead to wasted sprints and confused teams. The AI doesn't intentionally deceive. It simply predicts what statistics might logically exist based on patterns in its training. This creates a dangerous mix of accurate information blended with confident fabrications.

To tackle this, cross-reference any ChatGPT-provided statistics with industry reports or academic sources. Question suspiciously specific percentages or overly convenient data points. Train your team to treat AI outputs as starting points for research, not final answers.

ChatGPT conversations aren't private meetings. While OpenAI has policies about data usage, anything you type potentially becomes training data for future models. This matters when discussing unannounced products, proprietary methodologies, or competitive strategies. Samsung learned this lesson after employees accidentally leaked sensitive code through ChatGPT prompts.^[1]

Smart teams create boundaries around ChatGPT usage. They distinguish between general product questions and confidential specifics. Instead of asking "How can we improve our patented algorithm X?" they ask about general optimization patterns. This extracts value while maintaining security.

ChatGPT's suggestions for your new feature sound brilliant. The UI patterns, the user flow, even the marketing copy feel perfect. But who actually owns these ideas? This question stumps many product teams. When AI generates content based on millions of training examples, intellectual property becomes murky territory.

Copyright laws haven't caught up with AI-generated content. ChatGPT pulls patterns from its training data, potentially echoing existing products or patents without attribution. That clever feature might mirror a competitor's patented process. The catchy tagline could accidentally match someone's trademark. Using AI output without consideration creates legal risks.

Protect your product by treating ChatGPT as inspiration, not final output. Transform suggestions significantly before implementation. Document your iteration process to show human creativity. When ChatGPT provides specific examples or references existing products, research their IP status before borrowing concepts.

ChatGPT tells you that Spotify's algorithm prioritizes songs based on "user happiness scores" measured through microphone detection of humming. It explains the feature in detail, even naming it "MoodMatch Technology." You're about to present this fascinating insight to your team when you discover it's completely made up. ChatGPT invented the entire feature. These fabrications happen constantly. ChatGPT doesn't lie intentionally. It simply fills knowledge gaps with plausible-sounding fiction. When asked about specific features or company practices, it combines real patterns with imagination.

Protect yourself by questioning specific feature claims, especially when they sound innovative or unexpected. If ChatGPT describes detailed implementations or names particular features, verify them independently. Real product features appear in company blogs, documentation, or press releases. If you can't find external confirmation, assume it's hallucination.

ChatGPT excels at many tasks but fails at others. Real-time user sessions need authentic reactions. Sensitive customer complaints deserve human consideration. Strategic decisions affecting team members' careers require nuanced judgment. Cultural considerations for global products demand lived experience. These situations expose AI's limitations.

Recognize scenarios where ChatGPT hinders rather than helps. Use it for initial drafts, not final user communications. Avoid it for diversity and inclusion decisions. Skip it when legal compliance requires human accountability. Your judgment about when to engage AI versus human intelligence becomes a crucial product skill.

Quality control turns ChatGPT from a risky tool into a reliable assistant. Start by establishing verification layers for different content types. Product specs need technical review, user stories require edge case analysis, and market insights demand source validation. Each output type gets its own quality checklist.

Create systematic review processes that catch AI blind spots. Technical reviewers check feasibility and constraints. UX designers validate user flows and accessibility. Legal examines compliance implications. This multi-perspective approach ensures comprehensive coverage.

Implement feedback loops that improve future prompts. Document which ChatGPT outputs required heavy revision and why. Track patterns in AI mistakes. Does it consistently miss security requirements or oversimplify integrations? Use these insights to refine prompts and prevent common issues.

ChatGPT becomes chaotic when everyone uses it differently. One PM may generate user stories with minimal context. Another may write novels for simple questions. Developers may get inconsistent outputs. Design may receive conflicting feature suggestions. Without collaboration guidelines, AI amplifies confusion instead of productivity.

To prevent this, establish team-wide ChatGPT conventions:

• Define standard context levels for different tasks. User stories need product vision and acceptance criteria. Bug reports require system specs and user impact.
• Create output format templates. Agree whether feature ideas come as bullet points or paragraphs. Standardize how ChatGPT should structure technical documentation.
• Develop shared vocabularies for your product. Define key terms, acronyms, and concepts once. Include these definitions in relevant prompts for consistency.
• Assign prompt champions for complex workflows. Let your best prompt engineer own competitive analysis templates while another masters user research formats.
• Build hand-off protocols between team functions. Show how discovery prompts create context for requirement prompts, which inform test case generation.