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Veracity Reveals the Sources Behind Its AI Fact Checks With 0-100% Reliability Scores

August 27, 2026
Artificial Intelligence
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Veracity is an open-source fact-checking system from Montreal’s Complex Data Lab that pairs a large language model with a web retrieval agent and refuses to answer without showing its evidence. Every claim comes back as a reliability score between 0% and 100%, a plain-English interpretation, a share recommendation gated at 60%, and a panel listing every source consulted along with its documented credibility. This breakdown covers the five-stage pipeline, the stack behind it, why the 60% cutoff is the most questionable decision in the design, what independent benchmarks say about the roughly 83% real-world accuracy ceiling for automated fact-checking, and how to evaluate any claim-scoring layer before you let it near a workflow.

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RAG Chunking and Parsing: Proven Fixes for Better Answers

August 9, 2026
Artificial Intelligence
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Retrieval systems are judged on their answers but built on their chunks. This guide covers the two steps that create them: parsing, which turns a file into usable text, and chunking, which cuts that text into the units your index stores. It compares fixed, recursive, structure-aware and semantic splitting, sets out workable size and overlap ranges, explains the metadata that turns a search result into a citable answer, handles the document types that defeat generic splitting, and shows how to measure retrieval with a gold question set before you trust it.

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RAG vs Fine-Tuning vs Long Context: Simple Smart Guide

August 9, 2026
Artificial Intelligence
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A business decision guide to RAG, fine-tuning and long context windows: what each one actually changes, how the costs compare at real query volumes, a six-question scoring matrix, the hybrid patterns mature teams use, and the governance obligations each option creates.

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Graph-Enhanced RAG: Powerful Architectural Patterns Beyond Vector Search in Production

May 17, 2026
AI
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Graph-enhanced RAG solves the multi-hop retrieval and entity reasoning failures that limit pure vector search in production. This guide covers five architectural patterns, engineering decisions, and governance requirements.

Retrieval Augmented Generation

Veracity Reveals the Sources Behind Its AI Fact Checks With 0-100% Reliability Scores

RAG Chunking and Parsing: Proven Fixes for Better Answers

RAG vs Fine-Tuning vs Long Context: Simple Smart Guide

Graph-Enhanced RAG: Powerful Architectural Patterns Beyond Vector Search in Production

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