Engineering notes: making an AI system tell the truth, not what you want to hear
NicheIQ team systematically reduces hallucination and pleasantries-driven output in LLM applications through structured prompting, lightweight output validation, and feedback-driven iteration—without model fine-tuning, and compatible with GPT-4, Claude 3, and Llama 3.
Core Objective: Decoupling Truthfulness from User Satisfaction
Traditional LLM applications often conflate behavioral metrics (e.g., click-through rate, dwell time) with correctness, implicitly optimizing for ‘plausible-sounding’ rather than ‘factually accurate’ outputs. NicheIQ proposes explicit architectural separation between truthfulness and likability as distinct optimization dimensions.
Key Engineering Components
- Three-Phase Prompting: Decomposes single-generation into ‘fact anchoring → reasoning expansion → linguistic polishing’. The first phase mandates citation of verifiable sources (e.g., knowledge base IDs or arXiv paper IDs) and prohibits vague phrasing (e.g., “some studies suggest”);
- Lightweight Output Validator: A hybrid rule-based + small-model (e.g., Phi-3-mini) layer performing three checks: entity consistency (temporal/spatial/personal coherence), logical contradiction detection (via chain-of-thought reverse verification), and external knowledge conflict scanning (against Wikidata API or verified fact tables from Hugging Face Datasets);
- Feedback Signal Injection: Human-annotated ‘truthfulness score’ (0–5) and ‘user satisfaction score’ (0–5) are injected as independent signals—one updates prompt template weights, the other tunes re-ranking logic—to prevent conflated optimization.
Empirical Results
On NicheIQ’s proprietary Idea Generation Benchmark (127 cross-domain open-ended questions with ground-truth factual constraints), this architecture boosts GPT-4-turbo’s factual accuracy by 38.2% (from 61.4% → 99.6%), while reducing user satisfaction by only 2.1%—outperforming baselines like temperature=0 or JSON mode alone.