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.
Dependency-free evidence core for AI agents: observation envelopes, provenance, memory continuity and claim gates to reduce hallucination drift.
BioReasoner: Training LLMs for grounded scientific reasoning. 0% hallucination rate on citations, 100% format adherence. Cross-domain polymathic insights via Scientific Tribunal evaluation.
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.
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).
A conceptual AI architecture for reducing hallucinations by enforcing invariant, source-anchored knowledge constraints during generation.
A hallucination-resistant Retrieval-Augmented Generation (RAG) system.
A pipeline that gives probabilistic guarantees for reducing contextual hallucinations in LLMs
A RAG system that knows when not to answer using concentration inequalities
Runtime patch that kills LLM loops, drift & hallucinations in real-time – works with any model (GPT, Grok, Claude, Llama, Mistral…)
Policy-constrained LoRA fine-tuning to reduce hallucinations in a billing-focused LLM, using a PayFlow (fictional SaaS) use case with before–after evaluation.
An RLHF-inspired DPO framework that explicitly teaches LLMs when to refuse, significantly reducing hallucinations.
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