Controlling LLM Hallucination in Policy-Wording Comparison: A 2026 Discipline for Indian Commercial Brokers

Generative AI has quietly become part of how commercial brokers and risk teams work with policy wordings. It is used to summarise a 60-page wording, to compare two insurers' terms, to answer a client's question about whether something is covered, and to draft the comparison tables that go into a renewal report. The appeal is obvious: wordings work is dense, slow and high-volume, and a model that reads and compares in seconds is genuinely useful. The problem is that the output of this work is, in substance, coverage advice, and the failure mode of large language models, hallucination, is at its most dangerous precisely where the output is coverage advice.

The 2026 evidence is sobering for anyone treating model output as reliable. Benchmarks across dozens of models report hallucination rates in a wide band, and even specialised legal-domain tools trained by major vendors show meaningful error rates on document-grounded tasks. A hallucination is not a typo; it is the model stating, fluently and confidently, something that is not in the source, an exclusion that is not there, a sub-limit it invented, a deductible it misread, a condition it omitted. In ordinary writing, a confident error is embarrassing. In a wordings comparison that a client relies on to decide which cover to buy, a confident error is a coverage failure waiting to surface at a claim.

The danger is sharpened by three features of wordings work specific to commercial insurance:

• Materiality of small differences. Whether a wording says 'arising from' or 'directly arising from', whether an exclusion is carved back by a write-back, whether the average clause applies, these turn on words. A model that paraphrases loses exactly the precision that determines cover.
• Plausibility of the error. A hallucinated exclusion or condition reads exactly like a real one. There is no spelling mistake to catch it; it is grammatically perfect and contextually plausible, which is what makes it dangerous.
• Reliance and the E&O chain. When a broker passes AI-derived coverage analysis to a client and the client relies on it, the broker has taken on the accuracy of that analysis. If it is wrong, the broker's professional indemnity exposure is live.

Hallucination is not a fixed property of a model; it is heavily influenced by how the model is used. The single biggest lever a broker or platform has is grounding, constraining the model to answer from a specific, supplied source document rather than from its training memory. This is the difference between asking a model 'what does a typical marine cargo policy exclude?' (an invitation to invent) and asking it 'in this specific wording, what is excluded, and quote the clause' (a constrained, checkable task).

The production pattern that implements this is retrieval-augmented generation: the wording is indexed, the relevant passages are retrieved in response to a query, and the model is instructed to answer only from those passages and to cite them. Done well, this collapses the error rate, because the model is no longer drawing on a vague statistical memory of what policies usually say; it is reading the actual text in front of it. Done badly, it merely creates an illusion of grounding while the model still confabulates around the retrieved text.

The practical determinants of whether grounding works are unglamorous but decisive:

1. Source quality and structure. If the wording is a scanned PDF that was poorly extracted, the model is reading garbage, and grounding on garbage produces grounded nonsense. Clean, accurately extracted, structured text is the precondition for everything downstream.
2. Retrieval accuracy. If the system retrieves the wrong clause, or misses the relevant one, the model answers confidently from the wrong basis. Retrieval failure is a silent failure; the answer still looks authoritative.
3. Citation and traceability. The model should not only answer but point to the exact clause it relied on, so a human can check the answer against the source in seconds rather than re-reading the whole document. An answer without a citation is an answer that cannot be efficiently verified, which in practice means it will not be.
4. Instruction discipline. The model must be instructed to say 'this is not addressed in the wording' rather than to fill the gap, because the most damaging hallucinations are confident answers to questions the document does not actually answer.

Even the best-grounded system is not a substitute for verification when the output becomes coverage advice; it changes verification from re-reading everything to checking the specific claims the model makes. A broker that adopts AI for wordings work without redesigning its verification process is not saving time, it is relocating risk to the claim. The process that keeps AI-assisted comparison defensible has a few components.

Verify the material, sample the rest. Not every line of an AI summary needs human checking, but every statement that the client will rely on to decide cover does. The exclusions, the sub-limits, the conditions precedent, the warranties, the deductibles and the differences between insurers in a comparison are the material claims; these are checked against the cited source text, every time. Lower-stakes descriptive summary can be sampled.

Check against the source, not against plausibility. The discipline is to open the cited clause and confirm the model's statement matches it, not to nod because the statement sounds right. Because hallucinations are plausible by construction, plausibility is exactly the wrong test. The whole value of grounded, cited output is that this check takes seconds; the whole danger of ungrounded output is that this check is impossible.

Keep a record of what was verified. When AI-derived analysis goes to a client, the broker should be able to show, if it is ever questioned, what the analysis was based on and that the material claims were checked against the wordings. This record is both good practice and the broker's defence if a coverage dispute later turns on the comparison.

Treat 'not addressed' as a real answer. A frequent and damaging failure is the model, or the broker, supplying an answer where the wording is silent. If a wording does not address something, the correct output is that it does not, and that silence is itself material information for the client and a prompt to seek an endorsement or a clarifying term.

Match the verification effort to the stakes. A high-limit programme, a manuscript wording, or a contentious coverage question warrants more verification than a standard package renewal. The process should be proportionate, not uniform, so that scrutiny lands where the exposure is.

The through-line is that AI shifts the broker's work from reading to verifying, and verifying is only fast and reliable if the underlying source is clean, structured and citable. A broker who keeps that discipline captures the productivity of AI without inheriting its error rate; a broker who skips it has built a faster way to give wrong advice.

Putting this together, the goal is a wordings workflow that is materially faster than manual reading and at least as reliable, which is achievable but only if the architecture is right. The wrong architecture is a general-purpose chatbot pointed at a folder of PDFs, asked open questions, and trusted. The right architecture treats the wordings themselves as a governed, structured source of truth and uses AI to interrogate that source under verification, not to replace it.

The components of a defensible workflow are consistent with the regulatory direction in India, where the sectoral AI governance approach and the MeitY India AI Governance Guidelines press on transparency, human oversight and accountability, and where a broker's data handling sits under the DPDP Act and Rules 2025. A workflow built on grounded, cited, verified output is, not coincidentally, also the workflow that satisfies those expectations, because explainability and human oversight are exactly what grounding and verification deliver.

A practical specification looks like this:

• A clean, structured wordings repository as the source the AI reads, so extraction quality is controlled rather than left to chance, and so the same wording is interrogated consistently.
• Retrieval-grounded querying with citation, so every answer points to the clause it came from and can be verified in seconds.
• A comparison capability that lines up how insurers treat the same exposure, exclusion or condition, with the source clauses visible, so differences are evidenced rather than asserted.
• A verification step built into the process, not bolted on, with the material claims checked against the source and a record kept.
• Proportionate human judgement retained for interpretation, because deciding whether an exclusion bites or a write-back helps a particular client is advice, and advice is the broker's job, informed by the AI rather than delegated to it.

This is precisely the layer Sarvada provides for commercial-insurance brokers and corporate risk teams: structured, searchable access to insurer wordings and the intelligence around them, so that AI-assisted comparison and summary are grounded in an auditable source of truth, citable back to the clause, and verifiable in seconds rather than improvised from an opaque model. Brokers who want the speed of AI on wordings without the hallucination exposure can Request Access to evaluate the platform as the grounded foundation for their comparison and renewal work.