GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
36views this month
Decision brief
`fact-checker` utilizes prompt chaining in Jupyter Notebook to fact-check Language Model outputs, enhancing the accuracy and reliability of responses.
Good fit when
- - When you need to verify the accuracy of assumptions made by a Language Model’s initial response through self-ask methodologies.
- - For scenarios where you want to improve trustworthiness and credibility of LLM-generated content using predefined assumptions validation.
Avoid when
- - If an immediate answer is required without the step-by-step reassessment process, as `fact-checker` involves sequential validation that could be time-consuming.
- - In situations where real-time interaction is critical and a delay from additional self-interrogation steps would not be beneficial for user experience.
- Pricing:
- unknown - The licensing information for `fact-checker` is unclear, indicating that further investigation into its legal usage might be required before implementation.
- Requirements:
- Requires Python and possibly Jupyter Notebook environment for running the provided IPython notebook script or command-line script.
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (1062d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/jagilley/fact-checkerSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A simple demonstration for fact-checking Language Model outputs through prompt chaining to verify the assumptions made in the initial response.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
`python3 fact_checker.py 'insert question here'`Source link
Tags
README
fact checking with prompt chaining This repo is a simple demonstration of doing fact checking with prompt chaining. How it works: you ask your desired LLM a question the LLM generates an initial answer to the question the LLM self interrogates what the assumptions were that went...
For agents
This page has a .md twin and JSON over the API.