The "confident idiot" problem: Why AI needs hard rules, not vibe checks
7 days ago
- #Steer
- #AI
- #Determinism
- AI systems often exhibit 'Confident Idiot' behavior, making plausible but incorrect assertions with high confidence.
- Current solutions like 'LLM-as-a-Judge' create circular dependencies and fail to address core issues like hallucination and sycophancy.
- The article advocates for deterministic approaches over probabilistic fixes, emphasizing the need for hard rules and assertions in AI systems.
- Examples include using actual code checks (e.g., `requests.get()` for URLs, parsing AST for SQL queries) instead of relying on LLM judgments.
- Steer, a Python library, is introduced as a solution to enforce hard guardrails and catch errors in real-time.
- Steer features a 'Teach' loop that allows users to correct and inject specific rules into the model's behavior without redeploying code.
- Steer is open-source (Apache 2.0), runs locally, and prioritizes privacy by keeping keys private.