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.

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

Hallucination-prune multiagent RAG for pharmaceutical knowledge bases

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.

A conceptual AI architecture for reducing hallucinations by enforcing invariant, source-anchored knowledge constraints during generation.

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

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

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

A pipeline that gives probabilistic guarantees for reducing contextual hallucinations in LLMs

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

Policy-constrained LoRA fine-tuning to reduce hallucinations in a billing-focused LLM, using a PayFlow (fictional SaaS) use case with before–after evaluation.

Runtime patch that kills LLM loops, drift & hallucinations in real-time – works with any model (GPT, Grok, Claude, Llama, Mistral…)

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

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