The Wisecube Approach to Enhancing AI Reliability

Did you know that 96% of business leaders believe AI will transform their decision-making for good? With such a vast number of leaders believing in AI, it must be credible and reliable. However, if AI outcomes fall short of accuracy, they can risk loss of funds, public trust, and reputation. Inaccurate and unreliable AI outcomes lead to an erosion of trust among the public and keep leaders from using it to automate their tasks.

Wisecube strives to make AI, particularly large language models (LLMs), reliable to provide error-free insights and speed up the research process. To turn this dream into a reality, Wisecube offers unique features with boundless benefits in biomedical research. These include cutting-edge hallucination detection tools, billion-scale knowledge graphs, claim extraction and categorization, and real-time LLM analysis.

Let’s explore Wisecube’s methodology for improving AI reliability and achieving highly accurate LLM outputs. This methodology is unique to Wisecube, designed carefully to eradicate LLM hallucinations.

The Wisecube Methodology

Wisecube carefully designed a methodology to beat the challenges of AI unreliability in healthcare. With its unique features, Wisecube methodology promises accurate healthcare claims, reducing the LLM error rate. Here are the distinct components of the Wisecube methodology:

Knowledge Triplets: A Finer Grained Approach

Traditional hallucination detectors use sentences or phrases to understand context and identify hallucinations.  Effective to some extent, conventional hallucination detectors struggle to capture finer details. To tackle this, Wisecube extracts claims in the form of knowledge triplets from AI content to detect hallucinations and make LLMs context-aware.

Knowledge triplets divide a sentence into <subject, predicate, object> format. This allows understanding of context instead of generating content based on only keywords. Contextual understanding surpasses traditional methods and makes AI generate reliable outputs. 

Consider a fictional patient summary to compare conventional methods with knowledge triplets. 

Subject: Leonard McCoy Predicate: underwent Object: Prostate TRUS biopsy

Knowledge triplets dissect who underwent what. Since the conventional method doesn’t break sentences into this format, it will only capture keywords like “Prostate TRUS biopsy” and generate a general result.

Understanding a patient’s condition and history accurately significantly impacts diagnosis and decision-making in healthcare. Knowledge triplets’ ability to organize information in an easy-to-understand format and capture relationships revolutionizes healthcare research. 

Unlocking the Power of the Semantic Data Model

A semantic data model structures data to represent its meaning and relationships, enabling communication between healthcare systems. The semantic data model integrates various data standards, including HL7, FHIR, ICD-10, and SNOMED CT, allowing simpler communication between healthcare systems. Data flow across healthcare institutions ensures medical practitioners and researchers have a complete view of the information they need to make decisions. 

Unified data from various sources supports the rapid evolution of healthcare practices, facilitating research and quick drug discovery. The structured format and its ability to capture relationships and extract deeper insights from data that might otherwise remain obscured. The semantic data model fosters connected and efficient healthcare research by promoting communication among healthcare standards and embracing standardized formats.

Semantic Data Transformation

Semantic data transformation refers to transforming data into the Resource Description Framework (RDF) format. RDF provides a foundation for representing data in an understandable format. RDF shapes LLMs’ interaction with data by transforming data into meaningful elements and defining a relationship between them. This enables deeper insights and informed decision-making. A semantic data pipeline executes this transformation, and stores transformed data into a graph database. The two important aspects of transforming data into RDF include:

1. Schema mapping: Bridges the gap between data sources and the target ontology or a standardized blueprint. Precise mapping allows accurate representation of information with relationships among data elements. Accurate representation prevents minute details from getting lost in many healthcare data and generates reliable decisions. 

2. Relationships: Capturing relationships allows the creation of a complex web of connections, emphasizing the link among elements. This performs a deeper data analysis and uncovers insights that would otherwise remain hidden in traditional text analysis. 

Claim Extraction & Categorization

Claim classification involves flagging LLM responses into relevant categories. Dividing LLM responses into categories serves two purposes:

  • It represents the degree of deviation of an LLM response from reality.
  • It acts as a guide toward LLM improvement. 

Wisecube’s claim extractor extracts claims from two sources, including:

  • Reference: Claims present in LLM knowledge base.
  • Response: Claims generated by LLMs in response to user search queries.

After extracting claims from these sources, Wisecube compares them against each other to categorize them accordingly. The four categories Wisecube uses to assess the factual integrity of an LLM include:


Claims that are present in both response and references are flagged as Entailment. Entailment claims indicate accurate outputs.


Claims that are present in LLM responses but disregarded by references are flagged as Contradiction. These claims represent LLM hallucinations.

Missing facts 

Claims present in references but absent in LLM responses are flagged as Missing facts. Missing facts represent gaps in LLM responses.


Claims present in an LLM response but neither contradicted nor confirmed by the references are flagged as Neutral claims. Neutral claims represent an opportunity to verify them against a knowledge graph for additional insights.

Claim extraction and categorization allow biomedical researchers to gain a deeper understanding of an LLM’s strengths and weaknesses, identify potential issues, and improve LLM performance.

The Hallucination Detection Workflow

Wisecube’s hallucination detection goes through a set of processes to verify the factual integrity of LLM responses. Below are the steps involved in the hallucination detection workflow:

1. LLM Response Generation

The process begins with LLMs generating responses against a user query. Responses act as raw materials for the hallucination detector, containing insights that need to be assessed. 

2. Claim Extraction

The hallucination detector then extracts claims in the form of knowledge triplets. These claims are extracted from LLM responses and its knowledge base, which consists of references that serve as the benchmark for truth for LLM responses.

3. Claim Comparison

After the extraction of claims, the hallucination detector compares them against each other. This step also involves categorizing response claims into Entailment, Contradiction, Neutral, and Missing facts based on their alignment with the references.

4. Optional Knowledge Graph Check

For added verification, claims can also be evaluated against a knowledge graph. Wisecube’s extensive library of healthcare datasets provides a comprehensive assessment of claims. 

5. AI Hallucination Metrics Computation

The hallucination detector computes hallucination metrics based on claim comparisons. These metrics quantify the factual correctness of an LLM and highlight the areas of improvement in an LLM.

Leveraging the Knowledge Graph for Reliable AI

Wisecube’s Orpheus aggregates crucial facts from a plethora of sources to boost the reliability of healthcare LLM systems. It is a foundational graph AI consisting of 35 different external data sources, 1M+ publications, 260M extracted facts from text, and 5B+ total facts. Orpheus serves as a knowledge cornerstone in Wisecube’s hallucination detection tool, allowing extensive claim verification and precise analysis of LLMs. 

Orpheus revolutionizes healthcare research by helping LLMs identify relationships among crucial facts and understand context. This allows in-depth hallucination detection and promises a reliable future of AI in healthcare.

Contact us today to unlock doors to innovative healthcare research with our cutting-edge methodology and develop trustworthy AI solutions.

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