Data Products

Accuracy refers to how correct and factual the output of generative AI is. It evaluates whether the information provided aligns with real-world facts, established knowledge, or specific query requirements.

Accurate output ensures users can trust the AI for decision-making, research, or content creation. High accuracy reduces misinformation and enhances reliability.

1. Cross-check facts against trusted sources.
2. Verify numerical data, dates, and other specific details.
3. Assess adherence to the query or task requirements.

Coherence measures how logically and consistently the AI output is structured. This includes evaluating sentence flow, clarity, and grammatical correctness to ensure the text is easy to understand.

Coherent output ensures readability and usability. It is especially important for applications like article generation, customer support, and creative writing.

1. Check for logical connections between sentences.
2. Ensure proper grammar, punctuation, and sentence structure.
3. Detect and remove redundant or contradictory statements.

Hallucination refers to instances where generative AI provides false or fabricated information that may seem plausible but lacks any factual basis. It is a key challenge in evaluating AI output.

Detecting and minimizing hallucinations is vital to prevent the spread of misinformation and maintain credibility. It is particularly critical in fields like healthcare, law, and academia.

1. Identify fabricated data, fake references, or unsupported claims.
2. Analyze whether the output aligns with the input context.
3. Implement fact-checking tools or human-in-the-loop systems.