Prompt
What should I use if I need policy checks on every prompt?
Latest observation
If you need policy checks on every prompt, use a middleware / gateway layer that sits in front of the model and evaluates each request before it’s sent to the LLM.
Typical setup:
- Input policy check: inspect every user prompt for disallowed content, secrets, PII, prompt injection, etc.
- Output policy check: inspect the model’s response before returning it to the user.
- Block, redact, or route based on policy results.
Common options:
- A guardrails layer in your app server
- An API gateway / proxy
- A dedicated LLM safety filter or content moderation service
If you want, I can help you design a simple request-flow diagram or recommend a specific implementation pattern.
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.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.