Do you build hallucination-proof AI assistants?
“Your loan is approved under Section 42 of the Banking Act 2025.” One problem: there is no Section 42.
That single hallucination triggered a regulator investigation and a six-figure penalty. In high-stakes domains like finance, healthcare, legal and compliance zero-error tolerance is the rule. Your assistant must always ground its answers in real, verifiable evidence.
1 – Why high-stakes domains punish guesswork
- Regulatory fines, licence suspensions, lawsuits
- Patient harm or misdiagnosis
- Massive reputational damage and loss of trust
When the error budget is effectively 0%, traditional “chat style” LLMs are not enough.
2 – The three-layer defense against hallucination
2.1 Retrieval-Augmented Generation (RAG)
- What it does – Pulls fresh text from authoritative sources (regulations, peer-reviewed papers, SOPs) before answering.
- Win – Grounds every claim in evidence; supports “latest version” answers.
- Risk – Garbage in, garbage out. A bad retriever seeds bad context.
2.2 Guardrail filter
- What it does – Post-processes the draft answer. Blocks responses that:
- lack citations
- creep into forbidden advice (medical, legal)
- include blanket “always/never” claims
- Win – Catches risky output before it reaches the user.
- Risk – Over-filtering if rules are too broad or vague.
2.3 Question sanitizer
- What it does – Rewrites the user prompt, removing ambiguity and hidden assumptions so retrieval hits the right documents.
- Win – Sharper queries ⇒ cleaner answers.
- Risk – Requires strong NLU to keep the chat natural.
Raw prompt > “Is this drug safe for kids?”
Sanitized prompt > “According to current Therapeutic Goods Administration (Australia) guidelines, what is the approved dosage and contraindication list for Drug X in children aged 6–12 years?”
✅ Figure: Good example – Sanitization adds age range, official source, and specific drug name
Rule of thumb: Use all three layers. One patch isn’t enough.
3 – Reference architecture
- Vector store & embeddings – Pick models that benchmark well on MTEB; keep the DB pluggable (FAISS, Pinecone, Azure Cognitive Search).
- Retriever tuning – Measure recall@k, MRR, NDCG; test different chunk sizes and hybrid search.
- Foundation model & versioning – Record the model hash in every call; monitor LiveBench for regressions.
- Guardrails – Combine rule-based (regex) and model-based tools (OpenAI Guardrails, Nvidia Nemotron Guardrails).
- Audit logging – Append-only logs of user prompt, retrieval IDs, model version, guardrail outcome.
4 – Measurement is mandatory 🧪
Track from Day 0:
- Exact-answer accuracy (human-graded)
- Citation coverage (every claim cited)
- Compliance errors (dosage mismatch, policy breach)
- Hallucination rate (uncited claims)
- Retrieval miss rate (index drift, ACL failures)
5 – Scaling safely
| Stage | Accuracy target | Traffic share | Human-in-loop |
| Shadow mode | ≥ 80 % observed | 0 % | 100 % offline review |
| Pilot / augment | ≥ 80 % | ~5 % | Mandatory review |
| Limited release | ≥ 95 % on top queries | ~25 % | Spot check |
| Full automation | ≥ 99 % + zero critical | 100 % | Exception only |
Auto-fallback to a human expert if any metric dips below threshold.
6 – Domain experts are non-negotiable
- Source curation – SMEs tag “gold” paragraphs; retriever ignores the rest.
- Prompt reviews – Experts catch edge cases outsiders miss.
- Error triage – Every failure labeled with why it failed (retrieval miss, guardrail gap, model hallucination).
Treat specialists as co-developers, not QA afterthoughts.
7 – Key takeaways
- Layer it on – RAG + sanitization + guardrails deliver the most robust defense.
- Measure everything – Strict, automated metrics keep you honest.
- Log & secure by default – ACLs, encryption, append-only audit trails.
- Scale with care – Stay human-in-the-loop until the data proves otherwise.
Nail these practices and you’ll move from a flashy demo to a production-grade AI assistant that never makes up the rules or facts.