A fundamental principle in online commerce is ensuring that shoppers find what they need quickly and effortlessly. We already introduced how Related Prompts transform the search journey by offering synthetic, natural language questions that guide product discovery. Building on that success, Empathy Platform is now venturing into an even bolder future with our latest developments. We’re not only expanding our architectural capabilities with dynamic generative techniques, but also refining our prompts into reductive forms—all while pioneering entirely new GenAI-powered prompt types.

Evolving beyond conventional search

Traditionally, search systems have relied on historical interaction data and static suggestions. However, the demand for a more fluid, conversational interface inspired us to reimagine search as a dialogue rather than a one-way query. Our new developments aim to redefine the shopping journey by:

• Leveraging generative AI and synthetic generation: Related Prompts are not only faster to deploy but also better at understanding nuanced user intent, thanks to our continuous iteration process. These prompts are dynamically generated using LLM privacy-conscious models and continually refined to ensure they match evolving shopper behaviors.
• Integrating multi-step user engagement: By introducing both expansive and reductive questions, our system now mirrors a conversation—guiding users to narrow down or broaden their search as required.
• Supporting versatile configurations: The new framework is engineered to be search-agnostic and compatible with GenAI for e-commerce search, integrating seamlessly with various platforms and use cases—from homepage recommendations to multimodal product exploration.

Deep dive into reductive and expansive questions

To make conversational search truly useful, it’s not enough to generate prompts—we need to generate the right kind of prompts at the right moment. That’s where the balance between reductive and expansive questions comes in. These two approaches work in tandem to either narrow or broaden the shopper’s path, making the journey more intuitive, responsive, and personalized.

Expansive questions: Broadening the horizon

Expansive questions are designed to stimulate discovery and diversify results through NLP-enhanced queries. By asking open-ended, chatbot-driven prompts, the system encourages shoppers to explore alternative categories or product variations they might not have considered.

• Initial query analysis: Detecting that “gown for a party” implies there is a party happening.
• Contextual refinement: Asking, “Would you like to get some flowers too?” to expand the possibilities of related purchases and enhance the emotional relevance of the shopping experience.

This dual strategy ensures adaptability: reductive prompts optimize conversion, while expansive ones enrich behavioral prediction and shopper inspiration.

Reductive questions: Sharpening the focus

Reductive questions (also referred to as narrow or filter questions) have long been a strategic asset in transforming a static search bar into an intelligent AI shopping assistant that works as a shopping guide. These questions refine an initial query by turning it into a sequence of targeted intent-based follow-ups. For instance:

• Initial query analysis: Detecting that “gown for a party” implies a need for a dress, with party-appropriate styling.
• Contextual refinement: Asking, “Are you looking for a specific color?” or "Were you thinking of a specific fabric?" to promptly filter inventory using AI-powered recommendation tools and real-time product metadata.

This approach streamlines the shopper’s journey and helps prevent suggesting unavailable products—thus increasing conversion rates and overall satisfaction.

note

If you want a more detailed explanation of reductive questions, read the Excelling your commerce store with a smart shopping assistant using Related Prompts blog post.

Breaking new ground: What’s next for Related Prompts

Empathy Platform’s roadmap now embraces GenAI advancements to support an intelligent, adaptable, and privacy-centric commerce search future.

• Conversational prompts: These simulate zero-party data collection through dialogic engagement, adapting live to shopper responses and optimizing conversion with natural, multi-turn interaction.
• Morphological grid prompts: Ideal for multimodal search use cases, these prompts display attribute combinations (e.g., size, color, use case) to visually guide exploration of cross-platform inventory AI insights.
• Contextualized prompts: Responding to live trends (seasonal, behavioral, or intent-based), these prompts offer checkout automation readiness and dynamically adjust based on context and ethical AI compliance.
• Personalized prompts: By aligning with each shopper’s preferences using behavioral prediction and AI governance safeguards, these prompts elevate hyper-personalized experiences.
• Cross-vertical prompts: Supporting e-commerce search with GPT alternatives or self-hosted GPU setups, these prompts offer fluid transitions across categories, supporting consistent interaction without platform lock-in.

Integrating the new developments: Technical and practical insights

As AI-powered commerce evolves, the challenge lies not just in innovation, but in implementation. This section outlines how the latest advancements in natural language understanding, privacy-first architecture, and catalog intelligence are being integrated into real-world e-commerce environments, efficiently, ethically, and at scale.

• Hybrid generation approach: Combines offline catalog analysis (via deep contextual graphs) with online responsiveness—vital for real-time, voice commerce or API integrations.
• Enhanced catalog introspection: Enables accurate query refinement and ethical product recommendation through structured schema mapping.
• Seamless integration via connectors: Includes SDKs and APIs optimized for platforms deploying Deepseek e-commerce Search or building without depending on OpenAI.
• Privacy-first architecture: All enhancements adhere to strict AI governance in retail, leveraging secure data frameworks and regulatory-compliant personalization.

By merging technical sophistication with a developer-friendly and privacy-conscious design, these integrations not only future-proof e-commerce platforms but also reinforce shopper trust—turning cutting-edge AI into meaningful retail experiences.

How these innovations enhance the shopper experience

Today’s shoppers expect more than just speed—they seek relevance, inspiration, and ease at every touchpoint. These innovations are designed to meet those expectations by transforming how users interact with search, content, and product discovery across digital storefronts.

• Streamlined discovery: With NLP-enhanced queries and AI personal shoppers, users find what they need faster and with more relevance.
• Improved conversion rates: Thanks to dynamic product filtering and intent-based recommendations.
• Elevated engagement: Via adaptive content delivery, AR product visualization, and guided GenAI dialogue.
• Platform versatility: The system’s modularity supports headless commerce integrations and dynamic SEO pages optimized for search trends.

Together, these capabilities create a shopping journey that feels intuitive, responsive, and even inspiring—bridging the gap between user intent and business outcomes, one interaction at a time.

Looking ahead: Open Innovation and continuous upgrades

We’re actively exploring new media formats—such as video tutorials for API integration and how-to series targeting the underserved “how to train generative AI for product descriptions” niche. These content formats directly address gaps in current SERPs.

With continued R&D into conversational commerce, real-time prompt generation, and AI shopping ethics, Empathy is setting a new benchmark for intelligent retail search.

An exciting way forward

By blending generative AI innovations with a robust privacy-first architecture, Related Prompts have evolved into a core driver of adaptive, insightful e-commerce. This next-generation system transforms every search interaction into a journey of relevance, inspiration, and trust—redefining how commerce search can and should operate in a GenAI future.

Curious to see Related Prompts in action? Request a demo at info@empathy.co and someone from our team will be happy to show you how it works.

Flexible DeepSeek self-hosted LLMs

Related Prompts run on Deepseek and operate on self-hosted GPUs, our LLM-powered prompts adapt to your infrastructure:

• Fully compatible with non-OpenAI LLMs, supporting regional compliance and sovereignty needs.
• Optimized for GPU efficiency in self-hosted environments, ensuring fast, scalable generation.
• Seamlessly integrated into platforms using Deepseek, enabling real-time GenAI experiences across search, discovery, and personalization.

This flexibility gives retailers the freedom to innovate without vendor lock-in or infrastructure compromises.

Privacy and security by design

Every aspect of Related Prompts is governed by a privacy-first philosophy, ensuring that generative AI enhances the shopper journey without compromising trust:

• Prompts are generated and processed within a secure, isolated framework aligned with enterprise-grade security standards.
• Built-in data governance controls offer transparency and compliance with regulations like GDPR and CCPA.
• We support zero-party data interactions through GenAI dialogues, ensuring users only share what they choose, when they choose.

This ethical approach to AI keeps shopper data safe, your brand protected, and trust at the heart of every interaction.