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.

✅ 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

1. Vector store & embeddings – Pick models that benchmark well on MTEB; keep the DB pluggable (FAISS, Pinecone, Azure Cognitive Search).
2. Retriever tuning – Measure recall@k, MRR, NDCG; test different chunk sizes and hybrid search.
3. Foundation model & versioning – Record the model hash in every call; monitor LiveBench for regressions.
4. Guardrails – Combine rule-based (regex) and model-based tools (OpenAI Guardrails, Nvidia Nemotron Guardrails).
5. 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

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.