Orphaned Rules - 1 Rule
“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
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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.