At Empathy, we’ve always envisioned a search solution that goes beyond online commerce to push the boundaries of what AI can achieve in ensuring factual accuracy. From innovations like our Related Prompts—designed to enhance product discovery through conversational and intuitive search experiences—to today’s focus on tackling the challenges of AI hallucinations, we’re committed to evolving search technology to better serve people and businesses alike.

As AI technology progresses, we’ve recognized the need to address one of its most critical challenges: the problem of AI hallucinations—instances where generative models produce plausible yet entirely false outputs.

Our response? Combining the strengths of retrieval-augmented generation (RAG) with fully formatted facts (FFF) to deliver factual responses that prioritize accuracy, context, and individual intent.

A commitment to ending hallucinations

When relying on AI to generate information, trust is non-negotiable. Errors in responses can lead to significant consequences, particularly in complex or sensitive fields. At Empathy, we are tackling this issue by developing systems where factual responses take precedence, ensuring that every output is grounded in verified data.

With the aim of ensuring factual accuracy in responses and ending ambiguity, Fully Formatted Facts (FFF) represent a novel approach in the evolution of AI-driven technologies. At their core, FFF prioritize correctness by presenting literal information in a structured, unambiguous format that resolves common challenges in generative AI, namely hallucinations and misinterpretations.

Generative AI’s creative power often comes with a drawback: the potential for hallucination, where outputs may seem plausible but lack factual accuracy. This is an outstanding challenge in scenarios where specialized or domain-specific terminology is involved. To address this, we have designed processes that integrate literal, non-generative outputs into the workflow, ensuring clarity and trustworthiness.

By combining the flexibility of retrieval-augmented generation (RAG) with the rigor of verified data, FFF bridges the gap between individual queries and precise, context-aware answers. This framework not only enhances the reliability of AI outputs but also aligns seamlessly with the complexity of human language and intent, redefining trust in generative AI applications.

Enhanced query understanding

Our approach begins with refining how queries are understood. By identifying and interpreting technical jargon, Empathy’s AI services ensure individual intents are accurately captured and matched to reliable sources. Queries are augmented with context-specific clarifications, creating a frictionless bridge between what people ask and the most relevant data available.

This focus on clarity drives measurable results. In experiments, our methods consistently deliver improved accuracy when handling complex, specialized queries. By anchoring generative AI within frameworks that emphasize literal correctness, we align creativity with factual reliability.

These innovations are more than technical achievements—they represent a commitment to building AI that people can trust. Whether helping professionals navigate intricate data or ensuring precise answers in high-stakes scenarios, our solutions redefine the balance between generative capabilities and factual grounding.

Privacy, ethics, and data security at the core

At Empathy, privacy and ethical considerations are foundational. As we expand our AI-driven solutions, we ensure that innovation aligns with our values and responsibilities.

Our privacy by design approach ensures that all generative and retrieval processes are conducted within a secure, privacy-first framework. We enable businesses to operate within their own private cloud infrastructure, granting individuals robust data governance.

A key factor for this is transparency through data provenance, which has always been part of our ethos. People can trust our system because it provides visibility into how prompts are generated and the specific data context used.

By prioritizing these principles, Empathy not only delivers cutting-edge AI but also maintains the highest standards of data ethics and privacy compliance.

Rethinking the search process

At the heart of our exploration lies a question: How can we improve the search process from its very inception to better meet people’s intentions? The answer came in three parts:

1. Generative interrogation phase
   When an individual launches a query, Empathy’s GenAI services engage in dialogue to refine and clarify their intent. This initial phase uses AI's generative power to ask the right questions, aligning individual needs with the most accurate results.

2. Transforming queries into jargon
   To connect individual intent with the indexed feed, natural language queries are transformed into source-specific terminology. This step builds a bridge to factual information, ensuring precision.

3. Factual responses over generative text
   By retrieving content directly from original sources, literal and accurate answers are delivered. This reduces ambiguity and ensures responses are grounded in the Fully Formatted Facts framework.

Tackling complex challenges with advanced RAG

Our innovations leverage advanced RAG frameworks, combining generative AI capabilities with retrieval-based systems to achieve unparalleled accuracy. Through reflection-based question augmentation, ambiguous queries are transformed into context-rich inputs.

For example, a query like "How can I optimize product search results for better conversion rates?" might contain technical terms prone to misinterpretation. The system identifies jargon, retrieves definitions, and integrates them into an augmented query to ensure accuracy in responses.

This iterative process enables our models to handle complexity without sacrificing precision, effectively bridging gaps that have long plagued AI-driven systems.

Trust through Open Innovation

Empathy's journey is fueled by a commitment to Open Innovation. Every leap we take is informed by collaborative projects and built-in-public initiatives. This perfectible process not only accelerates the development of AI-powered features but also ensures that our solutions align with the evolving needs of the market. The RAG with Factual Responses model is one of many outcomes from this commitment, reflecting our dedication to transparency, accuracy, and people-centric innovation.

Through experiments and implementations, we’ve incorporated feedback loops and accuracy-boosting mechanisms into our tools. This ensures that AI hallucinations are minimized, and the search journey remains intuitive and reliable.

Try it out!

Love AI. Love privacy. Type away! on our search bar above and try out this new Privacy-First Generative Content Search right now! Use the toggle button to change between the two types of responses available: factual versus generative, and test this factual new approach to put an end to hallucinations.

Keep reading!

Discover more about how we are putting the latest discoveries in artificial intelligence and more into motion in our Empathy’s Open Innovation projects.

Learn more on our Open Innovation page (opens new window)