I jumped straight to RAG like it was a magic fix and the interviewer just waited.
Start by framing hallucinations as a system-level failure with multiple causes (data, model, inference, and user interaction), then walk through concrete mitigations at each layer of the stack. Emphasize trade-offs between accuracy, latency, cost, and user experience, and highlight the importance of continuous evaluation and monitoring.
Pro tip: Don't just list techniques—tie each mitigation to a specific failure mode and quantify the expected impact (e.g., 'RAG reduces factual hallucinations by X% but adds Y ms latency'). This shows you think in terms of engineering trade-offs, not just buzzwords.
Establish clear metrics (e.g., factual consistency, faithfulness to source) and build an evaluation pipeline with human and automated checks to baseline and track improvements.
Improve data quality, use retrieval-augmented generation (RAG) with authoritative sources, and implement query rewriting and re-ranking to ground responses in facts.
Fine-tune or prompt the model to be more factual (e.g., chain-of-thought, self-consistency), use constrained decoding, and consider ensemble or verification models to cross-check outputs.
Apply fact-checking modules, rule-based filters, and confidence scoring to flag or correct low-confidence outputs before they reach the user.
Deploy continuous monitoring for hallucination rates, collect user feedback, and iteratively update the system to address new failure modes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Systematically diagnose the RAG pipeline by isolating each component—retrieval quality, prompt construction, and generation—to identify where the contradiction arises. Then propose targeted fixes such as improving retrieval relevance, enforcing grounding via prompt engineering or constrained decoding, and adding post-hoc verification.
Pro tip: Emphasize that hallucination in RAG is often a retrieval problem, not a generation problem—measure retrieval precision/recall before blaming the model. Also, mention that you'd set up an evaluation harness with faithfulness metrics to catch regressions.
Check if the retrieved passages actually contain the answer and are relevant to the query. Compute retrieval metrics like recall@k and precision, and inspect the top-k passages for noise or missing information.
Ensure the retrieved passages are correctly inserted into the prompt, clearly separated, and that the model is instructed to rely solely on them. Look for truncation, ordering issues, or ambiguous instructions.
Test the model with the same context but different prompts or decoding parameters (e.g., temperature, top-p) to see if it ignores or contradicts the context. Check for known issues like recency bias or over-reliance on parametric knowledge.
Apply techniques like constrained decoding, citation enforcement, or post-hoc entailment checks to ensure the answer is supported by the retrieved passages. Consider fine-tuning or using a smaller model that adheres better to context.
Set up continuous evaluation with faithfulness and answer correctness metrics, and log cases where contradictions occur. Use this feedback to refine retrieval, prompting, or model choice.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining what hallucination means for your specific use case and how you'll measure it, then design a pipeline that automatically runs evaluation suites on every model or prompt change, using a combination of automated metrics and human-verified ground truth. Emphasize the importance of a golden dataset, continuous monitoring, and statistical rigor to detect regressions.
Pro tip: Use a stratified sample of prompts covering different difficulty levels and edge cases, and track not just overall hallucination rate but also per-category rates to catch subtle regressions. Also, consider using an LLM-as-a-judge with a well-crafted rubric as a scalable proxy, but validate it against human labels periodically.
Clearly define what constitutes a hallucination for your application (e.g., factual inaccuracy, unsupported claim) and select metrics such as hallucination rate, precision, recall, or F1. Decide on a threshold for acceptable performance.
Curate a diverse set of prompts with known correct answers or reference outputs, ensuring coverage of edge cases and varying difficulty. This dataset serves as ground truth and should be version-controlled and regularly updated.
Integrate the evaluation into your CI/CD pipeline so that any model or prompt change triggers the evaluation suite. Use automated metrics (e.g., exact match, semantic similarity, LLM-based judging) and compare against the golden dataset.
Compute hallucination rates and other metrics, and set up alerts for statistically significant deviations from the baseline. Use A/B testing frameworks to compare versions and identify root causes.
Continuously refine the golden dataset based on new edge cases and user feedback, and periodically re-validate automated judges against human evaluation to ensure they remain accurate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining when abstention is appropriate: high uncertainty, insufficient context, or high-stakes domains where errors are costly. Then explain a practical approach to calibrate abstention, such as training with an abstention token, using confidence thresholds, or leveraging uncertainty quantification, while balancing refusal rates with utility. Emphasize evaluation with metrics like selective accuracy and coverage to avoid over-refusal.
Pro tip: Frame abstention as a product decision: the right threshold depends on the cost of a wrong answer versus the cost of not answering, so you should tune it to the application and monitor it in production.
Identify scenarios where the model should say 'I don't know': out-of-distribution inputs, ambiguous queries, insufficient context, or high-risk domains where errors are unacceptable.
Select a method to enable abstention, such as training with an explicit 'I don't know' token, using confidence scores from the model, or applying post-hoc uncertainty estimation like entropy or ensembles.
Tune the confidence threshold or decision rule using a validation set to balance coverage (answering when possible) and accuracy (avoiding wrong answers).
Measure performance with metrics like selective accuracy, coverage, and refusal rate, and adjust the mechanism to avoid over-refusal while maintaining reliability.
Deploy with monitoring to detect distribution shifts and update the abstention policy as needed, ensuring it remains aligned with user needs and safety requirements.
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Frame the answer around the shift from probabilistic text generation to deterministic, side-effectful actions, where errors become irreversible. Discuss how mitigations must move from post-hoc filtering to pre-execution validation, sandboxing, and human-in-the-loop for high-risk operations, while balancing autonomy and safety.
Pro tip: Emphasize that in agentic systems, hallucination mitigation is not just about correctness but about safety and trust—design for graceful failure and auditability, and treat every action as a potential security event.
Explain that agents can cause real-world harm (e.g., API calls with side effects, code execution) and that hallucinations can lead to irreversible actions, data corruption, or security breaches.
Describe how mitigations must validate the agent's intended actions before execution, using techniques like schema validation, permission checks, and dry-run simulations.
Propose a defense-in-depth approach: sandboxing, rate limiting, rollback mechanisms, and human approval for high-stakes actions, tailored to the action's risk level.
Highlight the need for logging, monitoring, and anomaly detection to catch hallucinations in action, and to enable continuous improvement of the agent's decision-making.
Discuss how stricter mitigations reduce autonomy and increase latency; propose adaptive strategies based on context, such as allowing low-risk actions without approval.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.