Prompt
Do I need to evaluate both hallucinations and faithfulness?
Latest observation
Usually, yes — if you want a complete evaluation of a generated answer, you should look at both:
- Hallucinations: whether the model states things that are unsupported or false.
- Faithfulness: whether the answer stays grounded in the provided source/context.
Why both?
They’re related, but not identical:
- A response can be faithful to the source but still contain a subtle hallucination if it adds unsupported extra detail.
- A response can have no obvious hallucinations but still be unfaithful if it misrepresents the source, omits crucial caveats, or draws an invalid conclusion from the context.
Practical rule
- If your task is answering from provided documents/context, prioritize faithfulness.
- If your task is general factual generation, prioritize hallucination detection.
- For most RAG / grounded QA evaluations, it’s best to measure both, because together they give a clearer picture of quality.
If you want, I can also explain the difference between faithfulness, factuality, and hallucination in one simple table.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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