Skip to main content
The Fact Engine verifies factual claims using deterministic methods first, without requiring LLM calls for most claims.

​
Features

  • TF-IDF semantic similarity - No LLM needed.
  • Keyword overlap analysis - Fast and deterministic.
  • Entity matching - Numbers, dates, names.
  • Citation extraction - With relevance scoring.
  • Negation detection - Catch contradictions.
  • LLM fallback - Only when confidence is low.

​
Usage

from qwed_sdk import QWEDClient

client = QWEDClient(api_key="qwed_...")

result = client.verify_fact(
    claim="The company was founded in 2020.",
    context="Acme Corp was founded in 2020 by John Smith in San Francisco."
)

print(result.verdict)       # "SUPPORTED"
print(result.confidence)    # 0.95
print(result.citations)     # [{"sentence": "...", "relevance": 0.98}]
print(result.methods_used)  # ["semantic_similarity", "entity_matching"]

​
Scoring methods

MethodWhat it checksWeight
Semantic similarityTF-IDF cosine distance0.25
Keyword overlapShared important words0.20
Entity matchNumbers, dates, names0.35
Negation conflictContradicting statements0.20

​
Why deterministic?

BEFORE (v1): 
Claim → LLM → "I think this is supported" 😕

AFTER (v2):
Claim → TF-IDF + Entity Match → SUPPORTED ✅
       (LLM only if confidence < 0.7)
No more hallucinated fact checks!