The 3 Ps of AI UX: Designing Seamless, Trustworthy, and Powerful AI Experiences

Artificial Intelligence (AI) is transforming technology, making it indispensable in everyday workflows. Yet, building AI products isn’t just about technical robustness—it’s about creating exceptional user experiences (UX) that seamlessly integrate AI into tools people rely on daily. The rapid adoption of Generative AI (GenAI) highlights a critical challenge: how do we align engineering excellence with user-centric design to create trustworthy, adaptable, and impactful systems?

Reflecting on my experiences with distributed systems and data engineering, I came across a Latent Space article introducing the 3 Ps of AI UX Design—Presence, Practicality, and Power. These principles resonate deeply with the 3 Ps of AI Engineering—Providers, Provenance, and Polyglot, which I’ve developed over years of solving data challenges. Together, these frameworks provide a comprehensive roadmap for creating AI systems that are technically robust, user-focused, and built to last.

AI should feel like a utility, not a feature—seamlessly integrated, always present, and ready to assist

The 3 Ps of AI UX Design

1. Presence

Presence is about making AI a seamless part of the user’s workflow. It should feel like a natural extension of their tools, reducing friction and enabling intuitive adoption. For instance:

• Code autocomplete tools in IDEs like Copilot or Cursor embed AI directly into the developer’s environment.
• AI-enhanced writing assistants, such as Grammarly, integrate within email clients and browsers.

The goal is clear: users shouldn’t have to think of AI as a separate tool—it should simply exist, seamlessly integrated, and ready to assist. AI should function more like a utility than a standalone feature, much like infrastructure operates as a cloud utility.

2. Practicality

Practicality ensures that AI solves real problems. Features must deliver tangible value and address specific pain points rather than feeling like mere gimmicks. Examples include:

• Summarizing long emails or documents for professionals.
• Helping marketers generate tailored, effective ad copy.

Practical AI UX requires an understanding of the user’s daily challenges and designing solutions that fit seamlessly into their workflows.

3. Power

Power highlights the transformative potential of AI. It’s about enabling users to accomplish tasks that were previously difficult or impossible. Think of:

• An AI-powered legal assistant that extracts case law from hundreds of documents in seconds.
• A creative AI that generates unique art or music, providing users with tools to explore ideas and concepts, unlocking new avenues for creativity they never had before.

When AI demonstrates its power, it becomes a compelling reason for users to adopt the product.

The 3 Ps of AI Engineering

Complementing UX design, the 3 Ps of AI Engineering address the technical backbone that supports AI products.

1. Providers

Providers are the data sources and infrastructures that fuel AI systems. They supply high-quality, diverse datasets essential for model training, fine-tuning, and real-time operations. Reliable providers ensure the accuracy, relevance, and robustness of AI applications, forming the foundation for impactful workflows.

For instance:

• Public datasets like Common Crawl are critical for training language models.
• Proprietary sources like Bloomberg provide curated financial data for industry-specific AI tools.
• Real-time integrations with platforms like e-commerce APIs for product availability or geolocation APIs for traffic patterns enable AI systems to provide actionable, contextually relevant insights.

The goal is clear: without reliable data providers, AI systems cannot deliver consistent and meaningful results.

2. Provenance

Provenance establishes trust in AI systems by clearly linking outputs to their source data and detailing the processes behind their generation. It is essential for transparency, accountability, and compliance, especially in high-stakes domains like healthcare, finance, and legal systems.

For example:

• In healthcare, an AI tool recommending treatments should reference the clinical guidelines or patient data it relied on, enabling practitioners to validate its decisions.
• In financial analysis, provenance ensures that market predictions are traceable to reliable stock data or historical trends.
• In AI-generated content, source verification helps mitigate risks of misinformation, allowing users to confidently rely on outputs.

By embedding provenance into workflows, organizations can ensure credibility and foster user confidence.

3. Polyglot

Polyglot engineering enables AI systems to seamlessly interact with diverse data sources, formats, and languages, making them adaptable across industries and use cases. This versatility allows AI to handle structured and unstructured data while delivering tailored insights.

For instance:

• An AI system that translates product descriptions across multiple languages while accounting for cultural nuances, enabling personalized shopping experiences for international customers.
• Tools like Snowflake enable AI systems to unify data from cloud warehouses, relational databases, and real-time streams, driving advanced analytics and scalable insights.
• Platforms like Descript streamline video production by transcribing audio, detecting speakers, and extracting metadata. Creators can edit videos using transcribed text and generate automatic overlays, enabling quick uploads to platforms like YouTube Shorts or TikTok.

The objective is flexibility: AI systems should operate smoothly in dynamic environments, ensuring interoperability and scalability.

Bridging the Gap: Synergy Between UX and Engineering

The 3 Ps of AI UX and 3 Ps of AI Engineering are complementary, with each set of principles reinforcing the other to create impactful, user-friendly AI products. Let’s expand on this synergy with more tangible examples:

Presence Needs Providers

To achieve seamless Presence in UX, engineering must prioritize reliable Providers. These data sources fuel the AI and ensure it performs accurately, contextually, and consistently.

Examples:

• GitHub Copilot: Integrated into IDEs, GitHub Copilot relies on OpenAI’s models and extensive open-source repositories to provide accurate, real-time code suggestions. Its success hinges on the quality and breadth of its data providers.
• Notion AI: By combining internal tools with APIs from platforms like Google Drive, Notion enhances summarization and content creation, seamlessly embedding AI into workflows.
• Perplexity AI: This search engine integrates real-time data from trusted providers like Wikipedia and scientific journals, ensuring users receive relevant, verifiable outputs.

Provenance ensures that every decision made by an AI system is traceable, accountable, and trustworthy.

Practicality Relies on Provenance

Practicality in UX depends on Provenance in engineering. Transparent AI outputs, clearly linked to their sources, build trust and accountability.

Examples:

• Otter.ai: Meeting transcriptions link summaries to the original audio, enabling users to verify outputs effortlessly.
• Grammarly: Suggestions are accompanied by clear explanations, building trust and helping users improve their writing skills.
• BloombergGPT: For financial insights, BloombergGPT references real-time stock data and curated reports, ensuring reliability and transparency in high-stakes environments.

Power Comes from Polyglot Engineering

Powerful UX requires Polyglot capabilities to interact with diverse data sources and deliver advanced functionality.

Examples:

• Intercom AI: Its support bots integrate seamlessly with multiple platforms like Slack and Salesforce, retrieving and responding to inquiries with contextually relevant data.
• Tableau with GPT: Tableau uses GPT to query datasets across spreadsheets, databases, and cloud warehouses, enabling deep insights regardless of data format.
• Duolingo Max: Duolingo’s multilingual chatbot provides contextual corrections and adapts lessons dynamically, leveraging polyglot engineering to deliver a rich, personalized learning experience.

A Unified Approach: Building the Future of AI Products

By aligning UX and engineering principles, we can create AI products that are not only technically robust but also deeply user-centric. Here’s how this unified framework could look:

1. Embedded Trust: Combine Presence and Provenance to ensure seamless integration with transparent, verifiable outputs.
   • Example: A legal AI tool could summarize contracts while linking every point to the original document, seamlessly embedding itself into a lawyer’s document review workflow while building trust in its outputs.
2. Tangible Value: Merge Practicality with Providers to deliver reliable, high-value features grounded in user needs.
   • Example: A medical AI tool that integrates with electronic health records (EHRs) to highlight patient risk factors and suggest treatments ensures practical value by leveraging a reliable AI provider with domain expertise.
3. Scalable Innovation: Align Power with Polyglot to unlock transformative capabilities across varied contexts.
   • Example: An AI assistant in enterprise environments can process multilingual customer feedback from different CRMs, enabling businesses to act globally without missing nuances.

This synergy creates a feedback loop: engineering expands possibilities for UX, while UX priorities inform engineering focus. For example, conversational interfaces like ChatGPT gain user adoption not just because of their advanced models but because of seamless integration into existing tools (Presence), practical utility (Practicality), and transformative capabilities (Power).

Putting the 3 Ps into Action

To design AI products that users love and create systems that stand out, focus on these actionable steps:

1. Map UX to Engineering: Start by identifying user needs and aligning them with engineering capabilities. Ensure that Presence in UX is supported by reliable Providers in engineering for seamless data flow and intuitive integration.
2. Build Trust into AI Outputs: Integrate transparency and explainability features such as provenance tracking and source identification, allowing users to verify results. In high-stakes domains like healthcare, finance, or legal, this level of trust can become a key competitive advantage.
3. Prioritize Versatility: Leverage polyglot engineering practices to enable your AI systems to handle diverse data sources and formats. This adaptability ensures your product can serve varied industries and use cases effectively.

By applying these steps, you can create AI systems that meet user needs, inspire trust, adapt to complex environments, and deliver meaningful value.

Conclusion

The 3 Ps of AI UX Design and AI Engineering together provide a powerful framework for building AI systems that users love. By embedding AI seamlessly into workflows, addressing real-world challenges, and unlocking transformative capabilities, we can create products that are both practical and impactful.

As AI adoption accelerates, bridging the gap between engineering and design will become increasingly critical. These frameworks complement one another, ensuring every innovation is not only technically feasible but also delightful to use. By integrating these principles, we can shape the future of AI—one seamless, practical, and powerful product at a time.