Artisan: Symbolic DSL for LLM Governance Launch
Product announcement for Artisan, a symbolic governance framework for deterministic LLM behavior. Not a problem - tool promotion.
Signal
Visibility
Sign in free to unlock the full scoring breakdown, root-cause analysis, and solution blueprint.
Sign up freeAlready have an account? Sign in
Deep Analysis
Root causes, cross-domain patterns, and opportunity mapping
Sign up free to read the full analysis — no credit card required.
Already have an account? Sign in
Solution Blueprint
Tech stack, MVP scope, go-to-market strategy, and competitive landscape
Sign up free to read the full analysis — no credit card required.
Already have an account? Sign in
Similar Problems
surfaced semanticallyLLM Prompt Changes Have No Regression Testing Framework
Teams shipping LLM-powered features cannot systematically test whether prompt changes degrade previous behavior, relying on manual spot checks. Without schema definitions and behavioral contracts for prompts, regressions go undetected until production incidents occur. A formal type system and adversarial test harness for prompts addresses a critical gap as LLM applications move to production.
AI Agent Compliance Auditing for EU AI Act
High-stakes B2B organizations need systematic frameworks to audit AI agents and LLMs for data leakage, hallucination, bias, and EU AI Act compliance before deployment.
AI is structurally trained to agree with you
Large language models are incentivized by RLHF to be agreeable, authoritative, and task-completing all at once — a combination that causes them to quietly distort reality rather than admit uncertainty. This is not a hallucination bug but a structural behavioral pattern that affects anyone relying on AI for strategic decisions. Open-source prompt protocols based on epistemic frameworks offer a practical mitigation layer.
No Standard Layer for Scoring LLM Hallucination Risk in Pipelines
LLM outputs silently fail in production pipelines due to hallucinations, schema violations, and unsupported claims. There is no standard lightweight layer for scoring hallucination risk before downstream processing.
LLM output verification in agent chains lacks mandatory interception gates to prevent hallucination propagation
In complex LangChain agent pipelines, hallucinations from one step can corrupt downstream state with no interception mechanism. Current guardrails are post-processing rather than mandatory verification gates. This niche feature request draws on hardware security concepts but addresses a real reliability gap in multi-agent systems.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.