Anti-hallucination research skill for Claude Code — admits uncertainty, extracts direct quotes before analysis, cites every claim, retracts unverifiable statements. Based on Anthropic's official guardrail techniques. By TheGEOLab.net

About Essays, and Poc on using runtime evidence to build cleaner code context for AI to reduce hallucinations.

Reduce hallucinations through first-principles reasoning, verification, self-critique, and explicit uncertainty for AI agents.

Genesis Governance OS The Operating System for Multi-Agent AI Inspired by Political Science, built to coordinate intelligent agents at scale.

Why Pure Vector Search is a "False Proposition" for RAG?

Self-healing RAG system that retrieves, verifies, and grades its own answers. Automatically rewrites queries and retries when outputs are weak, ensuring accurate, hallucination-free responses.

Hallucination-prune multiagent RAG for pharmaceutical knowledge bases

span is a CLI tool for AI agents that moves blocks text addressed by reference to save context and ensure faithful byte-level edits within a file or between multiple files.

Dependency-free evidence core for AI agents: observation envelopes, provenance, memory continuity and claim gates to reduce hallucination drift.

Autonomous AI research agent using LangGraph to eliminate LLM hallucinations via a Generate-Critique-Refine self-reflection loop.

Developer-first prompt engineering patterns for grounded, testable, and reliable AI outputs.

Prompt engineering framework + evaluation harness for LLM workflows (classification, summarization, extraction).

LLM orchestrates SymPy for exact computation neuro-symbolic pipeline that routes math to symbolic solver, reducing hallucination on engineering problems.

Professional cross-agent answer quality gate for improving AI responses: intent match, evidence, assumptions, verification, brevity, and usefulness.

An RLHF-inspired DPO framework that explicitly teaches LLMs when to refuse, significantly reducing hallucinations.

A hallucination-resistant Retrieval-Augmented Generation (RAG) system.

A RAG system that knows when not to answer using concentration inequalities

Building human-first AI systems that transform trust from a score into an explainable and actionable insight.

BioReasoner: Training LLMs for grounded scientific reasoning. 0% hallucination rate on citations, 100% format adherence. Cross-domain polymathic insights via Scientific Tribunal evaluation.

C.A.R.A. has reached 248 million people globally. - Selcuk Cara embodies the rare manifestation of a modern universal scholar. IQ score of 185+, validated by two distinct international centers of excellence in giftedness research, forms the basis for a high-performance, real-time synthesis of scientific, aesthetic, and phenomenological ontologies.