The "confident idiot" problem: Why AI needs hard rules, not vibe checks

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

The three most prevalent themes in the discussion are:

The Fundamental Nature of LLMs as Statistical Predictors, Not Fact Engines: Many users emphasize that LLMs operate on language statistics rather than verifying objective truth, leading directly to the problem of hallucination.
- Quotation: Young, one user summarized this core issue by stating, "You can't blame an LLM for getting the facts wrong, or hallucinating, when by design they don't even attempt to store facts in the first place. All they store are language statistics, boiling down to 'with preceding context X, most statistically likely next words are A, B or C'." (HarHarVeryFunny)
The Counter-Strategy: Bounding Non-Determinism with External Deterministic Checks: The original poster and subsequent commenters proposed solutions focused on managing the inherent probabilistic nature of LLMs by wrapping them in deterministic software engineering constraints (assertions, verifiers, structured output).
- Quotation: The original author proposed this bounding concept: "If we wrap the generation in hard assertions (e.g., assert response.price > 0), we turn 'probability' into 'manageable software engineering.' The generation remains probabilistic, but the acceptance criteria becomes binary and deterministic." (steerlabs)
The Inevitability of Adversarial Degradation and the Need for Verification: Several discussions circled back to the idea that LLMs, like previous algorithmic systems (e.g., PageRank), will inevitably be gamed or poisoned by adversarial content, necessitating constant external validation.
- Quotation: One user noted the practical extension of this: "As long as you try to use content to judge search ranking, content will be changed, modified, abused, cheated to increase your search rank. The very moment it becomes profitable to do the same for LLM ‘search’, it will happen." (mrguyorama)

🚀 Project Ideas

Deterministic Agent Process Verifier (DAP-V)

Summary

A framework and lightweight service designed to wrap LLM calls, automatically executing a series of explicit, non-LLM verification steps against the LLM's output or required inputs/context before the final (potentially high-stakes) reasoning task begins.
Core value proposition: Transform probabilistic agent setup into manageable, deterministic software engineering by verifying grounding preconditions, dramatically reducing "unforced errors" common in complex agent workflows.

Details

Key	Value
Target Audience	Developers building LLM-powered agents, especially for high-reliability internal tools or regulated workflows.
Core Feature	A pre-execution engine that validates context integrity (e.g., file paths, IDs, schema adherence) using standard code/APIs before invoking the main LLM reasoning step.
Tech Stack	Python (for easy integration with orchestration frameworks like LangChain/LlamaIndex), Pydantic (for schema verification), standard file system/database query libraries.
Difficulty	Medium
Monetization	Hobby

Notes

Why HN commenters would love it: It directly implements steerlabs' thesis: "If we wrap the generation in hard assertions... we turn 'probability' into 'manageable software engineering.'" It addresses the concern that AI agents "eventually forget" rules/preconditions by enforcing them externally and deterministically.
Potential for discussion or practical utility: This tool directly addresses the desire by commenters like jkubicek for rules to be "embedded in an API" rather than relying on in-context commands that might be ignored. It formalizes the pre-flight checklist identified by steerlabs.

Source-Aware Context Grounding Engine (SACGE)

Summary

A service that analyzes the initial prompt/context provided by the user and automatically executes preliminary searches or database lookups before synthesizing the final LLM prompt, ensuring grounding in verifiable data, not just statistical language patterns.
Core value proposition: Mitigates reliance on the LLM's memory of facts (which commenters like HarHarVeryFunny argue are flawed) by grounding it in external, controlled, and attributable data sources identified upfront.

Details

Key	Value
Target Audience	Users who need factual grounding but want to move beyond simple RAG; developers working in niche domains where LLM training data is unreliable (`coldtea`'s observation about niche expertise).
Core Feature	A lightweight orchestration layer that parses key entities from the user query, fetches required documents/data structures (e.g., internal documentation, API data), and injects citations/source metadata directly into the prompt context.
Tech Stack	Python/Node.js, Vector Database (e.g., Chroma, Pinecone) for internal data retrieval, standard API clients.
Difficulty	Medium
Monetization	Hobby

Notes

Why HN commenters would love it: This directly supports DoctorOetker's call for "Source-aware training" by implementing source awareness at the inference layer if base model changes aren't feasible. It forces external verification, contrasting with the LLM's internal statistical prediction.
Potential for discussion or practical utility: This service naturally lends itself to generating outputs that explicitly list sources for every asserted "fact," leading to more transparent and less purely "sycophantic" responses that wisty lamented.

Conversational Flow Controller (CFC) Interface

Summary

A dedicated UI/API wrapper designed to control the turn-taking dynamics between the user and the LLM, forcing the model into a clarification/planning phase before attempting final generation.
Core value proposition: Addresses systemic frustration with LLMs that "spill out answers" (keiferski) by enforcing a structured, multi-turn "ask first, then answer" conversational pattern, treating the LLM as an inquisitive partner rather than an immediate response machine.

Details

Key	Value
Target Audience	Users who find existing chat UIs suboptimal for complex problem-solving that requires iterative refinement (e.g., architecture discussions, complex coding tasks).
Core Feature	A "Plan Request Mode" that defaults model temperature toward higher settings for brainstorming, mandates the LLM to first output a series of questions to finalize requirements, and prevents auto-submission until the user explicitly approves the finalized plan/context block.
Tech Stack	Frontend (React/Svelte) for superior input control (e.g., `shift-enter` handling), API Gateway managing turn state and context reset.
Difficulty	Low
Monetization	Hobby

Notes

Why HN commenters would love it: Solves the issue keiferski and monerozcash cited regarding LLMs not engaging in the "human dance" of clarification. It provides a tool to fight the "info dump" effect mentioned by falcor84 and HPsquared.
Potential for discussion or practical utility: This focuses on the UX of interaction. It proves that much of the "bad behavior" is due to the interface (chat style) rather than just the model weights, which might spark debate on optimal interaction paradigms (lkbm).