How does it work?

When you perform a fact check with FactSnap, two processes begin simultaneously to analyze your highlighted claim:

  1. Preliminary Check: FactSnap sends the text to Groq (a platform that facilitates the interaction with the language model LLama 3.3) and performs a super-fast, short preliminary analysis of the claim. This result is labeled as “Preliminary Check” in your result dialog.

  2. Detailed Check: At the same time, FactSnap uses exa.sh to search the web for relevant sources related to the claim. Once exa.sh identifies sufficient resources, FactSnap employs GPT-4o-mini to evaluate the claim against those sources, providing a detailed fact-checked response. This is displayed as “Explanation” in your result dialog.

After the detailed explanation with supporting sources is ready, FactSnap updates the preliminary result based on the more comprehensive findings.

When you perform a fact check with FactSnap, two processes begin simultaneously to analyze your highlighted claim:

  1. Preliminary Check: FactSnap sends the text to Groq (a platform that facilitates the interaction with the language model LLama 3.3) and performs a super-fast, short preliminary analysis of the claim. This result is labeled as Preliminary Check in your result dialog.

  2. Detailed Check: At the same time, FactSnap uses exa.sh to search the web for relevant sources related to the claim. Once exa.sh identifies sufficient resources, FactSnap employs GPT-4o-mini to evaluate the claim against those sources, providing a detailed fact-checked response. This is displayed as Explanation in your result dialog.


After the detailed explanation with supporting sources is ready, FactSnap updates the preliminary result based on the more comprehensive findings.

When you perform a fact check with FactSnap, two processes begin simultaneously to analyze your highlighted claim:

  1. Preliminary Check: FactSnap sends the text to Groq (a platform that facilitates the interaction with the language model LLama 3.3) and performs a super-fast, short preliminary analysis of the claim. This result is labeled as “Preliminary Check” in your result dialog.

  2. Detailed Check: At the same time, FactSnap uses exa.sh to search the web for relevant sources related to the claim. Once exa.sh identifies sufficient resources, FactSnap employs GPT-4o-mini to evaluate the claim against those sources, providing a detailed fact-checked response. This is displayed as “Explanation” in your result dialog.

After the detailed explanation with supporting sources is ready, FactSnap updates the preliminary result based on the more comprehensive findings.

Which Language Models (LLMs) power FactSnap?

  • For the preliminary check (fast response), FactSnap uses the language model LLama 3.3.

  • For finding relevant sources, it uses exa.sh.

  • For the detailed check (detailed explanation) based on the sources, it uses GPT-4o-mini.

How accurate is the fact-checking?

LLMs can be useful for fact-checking by quickly analyzing and summarizing information, but their reliability depends on the quality and recency of their training data. They should always be used alongside verified sources, as they may sometimes propagate outdated or inaccurate information.

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© Studio NAND 2025 | Data Privacy

Created by Studio NAND to enable better informed decisions. Got feedback?

Let's talk

We are part of the AI4Democracy initiative.
Find out more!

Visit website

AI for Democracy
AI for Democracy

© Studio NAND 2025 | Data Privacy

Let's talk

Created by Studio NAND to enable better informed decisions. Got feedback?

Visit website

We are part of the AI4Democracy initiative. Find out more!

AI for Democracy
AI for Democracy

© Studio NAND 2025 | Data Privacy