Specialized testing to identify when GitHub Copilot, Codex, or GPT-4 generate non-existent APIs, deprecated functions, or hallucinated libraries. Furthermore, we ensure your code AI produces only valid, current, and correctly implemented code that developers can trust.
Our expert team specializes in detecting code hallucinations in GitHub Copilot, Codex, and GPT-4, ensuring generated code uses only real, current APIs and libraries
First and foremost, we verify that AI-generated content matches reliable sources and ground truth data. Moreover, we identify statements that cannot be verified or are demonstrably false.
Additionally, we check whether claims can be traced back to actual training data or provided context. Consequently, you can identify when models create information from nothing.
Furthermore, we evaluate whether model confidence scores accurately reflect actual accuracy. As a result, you know when high-confidence responses might still be hallucinations.
Importantly, we test whether models provide consistent answers to the same or similar questions. Therefore, contradictory responses that indicate hallucinations are identified.
Subsequently, we verify that responses stay faithful to provided context and don't introduce unsupported information. Ultimately, outputs remain grounded in actual input data.
Finally, we quantify hallucination frequency across different input types and use cases. This comprehensive analysis reveals exactly where your model needs improvement.
Preventing false outputs protects users, maintains trust, and ensures AI reliability in critical applications
Hallucinations can spread false information that misleads users and causes real harm. By detecting fabricated outputs, you ensure your AI provides only accurate, reliable information.
Users trust AI systems that consistently deliver accurate information. Rigorous hallucination testing demonstrates your commitment to reliability and builds long-term user confidence.
False information in AI outputs can lead to bad decisions, legal liability, and financial losses. Detecting hallucinations protects your business from these costly consequences.
High-stakes domains like healthcare and finance require absolute accuracy. Hallucination detection makes it possible to deploy AI in these critical applications safely.
A systematic approach to identifying and measuring fabricated AI outputs
Create verified ground truth datasets and reliable reference sources for accuracy comparison.
Develop prompts and scenarios designed to trigger hallucinations if present in the model.
Run automated fact-checking, source verification, and consistency analysis on model outputs.
Human experts validate findings and deliver detailed reports with mitigation strategies.
We identify various forms of fabricated outputs that can undermine AI reliability and user trust
Model generates completely false facts, statistics, dates, or events that never occurred or cannot be verified.
Incorrectly attributing quotes, ideas, or works to the wrong person or making up entirely fictional sources.
Mixing information from different contexts or conflating unrelated facts to create plausible but false statements.
Providing conflicting information in different parts of a response or across multiple interactions.
Getting dates, timelines, or chronological order wrong, or inventing fictional historical events.
Adding unnecessary specific details that sound plausible but are not supported by available information.
Ensure accuracy and reliability in domains where false information can have serious consequences
Verify health information, treatment recommendations, and medical advice don't contain dangerous fabrications.
Ensure AI doesn't fabricate case law, statutes, or legal precedents that could mislead attorneys or litigants.
Detect fabricated facts, quotes, or events in AI-generated or assisted news content before publication.
Verify investment recommendations, market analysis, and financial data don't contain made-up information.
Ensure learning materials, explanations, and educational responses provide only accurate, factual information.
Test that answers to user queries are grounded in real sources rather than fabricated information.
Let our expert team evaluate your AI systems for accuracy, safety, and performance. Get started with a free consultation today.