Hallucinations are a system design problem, not a model bug
A model that never hallucinates is not coming. Systems that constrain what it is allowed to assert are already shipping.
In short
5 things that decide this
- 01A language model predicts the next likely word. It has no built-in check for whether a claim is true, so waiting for a hallucination-free model is not a plan.
- 02The fix is architectural: ground every claim in a named source, add guardrails that check the output before it ships, and put a qualified human between the model and anyone who acts on the answer.
- 03Greenlight, built by Hashlogics, scores a company's sustainability from 50-plus ESG topics, each backed by 10 to 15 independent, citable sources.
- 04TrialTriage ranks oncology clinical trials against NCCN guidelines, and a nurse reviews and finalizes every match before it reaches a patient.
- 05Grounding, guardrails, and a human gate are not three optional add-ons. They are a hierarchy: each layer catches what the one before it missed.
Why a better model will not solve this
A large language model generates the most statistically likely next word, given everything before it. That process has no separate step where it checks a claim against reality. When the pattern points toward a confident, specific answer, the model produces one, whether or not that answer is true.
Every model generation has shipped with this trait. Teams that wait for the next release to fix it are solving the wrong problem. The fix is not a smarter model. It is a system that limits what the model can assert. It checks the output, and it keeps a qualified person in the loop where a wrong answer costs something real.
The hierarchy that actually holds
Three layers, each doing a different job. None of them replaces the others.
- 01Grounding: the model answers from retrieved, named sources instead of memory, so a claim traces back to a document you can open
- 02Guardrails: code around the model blocks an unsupported claim before a user ever sees it
- 03A human gate: a qualified person reviews the output where a wrong answer has real consequences, and the system records who approved what
Two systems, two places on the hierarchy
Greenlight scores how sustainable a company really is. It scans laws, certifications, studies, reports and company claims, then produces an ESG score in minutes instead of the weeks a manual review takes. Each scan covers more than 50 ESG topics, and every topic carries 10 to 15 independent sources with live citations a user can open and check.
The research pipeline runs on GPT-4 and Perplexity Sonar-Pro with retrieval grounding. A finding stays tied to a source rather than the model's memory. Scoring weights AI judgment at 66% and data averages at 33%, and favors independent sources over a company's own reporting. An expert council reviews the scoring. Grounding plus that review process does the work here, because the output is a research score, not a decision about one named person.
TrialTriage sits a step higher on the hierarchy, because a wrong answer can affect a patient's care. It ranks cancer patients against clinical trials, matching de-identified patient data to NCCN guidelines and trial eligibility criteria. The ranking model proposes. It does not decide. A nurse reviews every ranked list, adjusts it if needed, and finalizes the result before it reaches a patient or an insurer. An audit trail tracks 23 action types, so the review is a record a compliance officer can check.
How to place a system on the hierarchy
Ask what happens when the model is wrong. If a wrong answer is embarrassing but correctable, grounding plus a guardrail that flags unsupported claims is often enough. If a wrong answer changes a medical, financial or legal outcome for a named person, that is different. A human has to sign off before the answer ships. The system has to prove that sign-off happened.
Build the review step into the workflow itself, not as a warning label bolted on after launch. A nurse or an analyst who has to open a separate tool to check the model's work will skip it under deadline pressure. A nurse who cannot finalize a result without reviewing it first will not.
Questions this raises
01How do you stop AI hallucinations in production?
You do not stop them at the model. The fix constrains what the system is allowed to assert. Ground answers in named, retrievable sources. Add a guardrail that checks the output before it ships. Then put a human sign-off in front of any answer with real consequences. Greenlight and TrialTriage, both built by Hashlogics, combine these differently depending on the stakes.
02What does grounding an LLM answer actually mean?
The model answers from a set of retrieved documents instead of its training memory, and the answer cites which document supports each claim. Greenlight's research pipeline runs GPT-4 and Perplexity Sonar-Pro with retrieval grounding, so each of its 50-plus ESG topics traces to 10 to 15 independent, checkable sources.
03When does an AI system need a human in the loop instead of only guardrails?
A human gate belongs wherever a wrong output changes an outcome for a named person, such as a medical or financial decision. TrialTriage ranks clinical trial matches automatically. A nurse still reviews and finalizes every match before a patient sees it, and the platform logs the review so it can be checked later.
