5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering

LLM hallucinations stem from lack of grounding, overgeneralization, and the model's tendency to always produce an answer. Five system-level techniques go beyond prompt engineering to address this: (1) Retrieval-Augmented Generation (RAG) anchors responses in external verified data via vector search; (2) Output verification layers use secondary models or self-consistency checks to validate responses before delivery; (3) Constrained generation uses JSON schemas and structured outputs to limit model freedom; (4) Confidence scoring uses token probabilities and explicit uncertainty signals to flag unreliable answers; (5) Human-in-the-loop pipelines route low-confidence or high-risk outputs to human reviewers. Each technique is illustrated with Python code examples and practical design patterns for production systems.