LLM hallucination prevention is mainly a system-design problem, and only secondarily a prompting problem. A language model writes text by approximating the statistical distribution of its training data. That makes confidently stated false content a natural result of how it works, and a formal result shows it cannot be eliminated in a model used as a general problem solver. What you can do is contain it. Deterministic engines should compute the numbers, make the decisions and sequence the actions. The LLM should narrate only what an explicit interface contract passes to it, with code checking the narration against that contract. In the enforcement studies below, the guarantees that held under testing came from enforcement code, not from the model or the prompt.

Why do AI hallucinations happen in the first place?

NIST’s Generative AI Profile (NIST AI 600-1) defines confabulation as “the production of confidently stated but erroneous or false content”, which is what people mean by hallucination. It calls confabulation a natural result of generative design. An LLM predicts the next token, and that can produce accurate output just as easily as output that is inaccurate or internally inconsistent. Factual accuracy is not the explicit goal of autoregressive generation. It only follows when token prediction is highly probable, over training data that is always flawed or incomplete.

A 2025 paper argues that models hallucinate because training and evaluation reward guessing over admitting uncertainty. When incorrect statements cannot be told apart from facts, ordinary statistical pressure produces hallucinations.

For anyone building a system, the consequence follows directly. When an error comes from the generation mechanism itself, you cannot instruct it away with text sent to that same mechanism. NIST also notes that LLMs sometimes set out logical steps for an answer even when the answer is wrong, which can further mislead people into trusting it.

Can AI hallucinations be fixed, or only contained?

They can be contained, not fixed. One formal analysis defines hallucination as an inconsistency between a computable LLM and a computable ground-truth function. Using learning theory, they show that such a model cannot learn every computable function and so will hallucinate. A taxonomy of LLM hallucinations draws the practical conclusion: computable LLMs cannot stop themselves hallucinating. Any mitigation that relies only on the model’s internal mechanisms, prompt-based chain-of-thought included, cannot remove it fully. A 2025 paper on the theoretical foundations sets the realistic goal: push the probability of hallucination low, then surround the model with detection and mitigation.

The Grounded Optimization study shows why the prompt alone is not enough. With all five layers active, deterministic checks plus prompt grounding, the overall detected hallucination rate was 0.04 to 0.24. Prompt-level grounding alone reached zero detected hallucinations, but only with a strong instruction-following model at temperature 0. The authors report that the result did not hold across models or temperatures. In one case GPT-4.1-nano, at temperature 1.0, wrote “vector databases” into a 2017 to 2019 banking role, while the same model kept to the date constraint at temperature 0. The deterministic layers added the most value exactly where prompt compliance was weakest, at high temperature and with weaker models. Treat the prompt as the first line of defence and the deterministic layers as what still holds when conditions drift.

How do you stop an LLM inventing numbers it was only meant to narrate?

Don’t let the LLM produce numbers at all. Build a staged pipeline. Deterministic forecasting, classification or rules stages compute every figure and every permitted cause, then pass them across a fixed interface contract to a generative stage whose only job is to draft the language. The LLM never sees raw inputs it could reinterpret, and code catches anything it writes that the contract does not contain.

TechnoLynx builds this separation through its Generative & Agentic AI R&D work for teams adding narration to forecasting, alerting or analytics pipelines. The first step is mapping the existing pipeline and the exact point where generated text is meant to enter it. From there the engagement defines the contract, builds the deterministic engine and the constrained narration layer, and tests the narration against adversarial and edge-case inputs before it goes into production reporting.

We keep the two stages apart because they fail in different ways. A forecasting or residual engine fails by being wrong about the world. A narration layer fails by saying something the engine never said. We prefer modular, transparent, observable systems to end-to-end magic, because when something breaks you need to know where and why. Once the stages are separate, a wrong sentence is either a contract violation or a faithful report of a wrong number. Those are different bugs with different owners.

Grid-Mind: fabrication around the tool boundary

Its pipeline produces a structured interconnection impact report: per-stage results, violation details, a recommendation and reason codes. The LLM writes its explanation from that report. Only the configured power-system solvers compute voltage values, thermal loadings and violation counts.

That was still not enough. Asked about maximum capacity, DeepSeek-R1 answered “approximately 127 MW” for a bus whose verified limit was 3.9 MW, without calling the capacity tool. The authors’ conclusion is the useful part: a factually grounded architecture is not enough on its own if the model can step around its tool interface.

Their fix was a three-layer defence, and only the first layer lives in the prompt:

1. System instruction hardening. The prompt forbids stating MW, per-unit, MVA or percentage values unless they came from a physics operator in the current conversation or are well-known published standards.
2. Forced action routing. A deterministic classifier in front of the LLM spots high-risk capacity questions and calls the binary-search capacity operator directly, bypassing the LLM for that kind of request.
3. Post-response grounding validation. A regex scanner looks for numeric claims in the output. If no grounding tool ran during the turn, it appends a disclaimer telling the user to request a simulation.

In the targeted ablation, routing covered every test case and no ungrounded numeric outputs were detected on the tested prompt sets. The authors say plainly that this is not a formal guarantee that every number is grounded. When numbers cannot be grounded, the intended default is to abstain and send the case to human review rather than issue an autonomous recommendation.

What the interface contract between engine and LLM should contain

The interface contract is the schema of everything the narration layer may say: computed values, where they came from, and the causes the upstream stage is allowed to attribute. Anything outside it cannot be asserted.

Contract element	What it prevents	Evidence
Every output field classified as deterministic (copied from source by software, no LLM allowed) or LLM-required	The model restating, and possibly altering, a fact software should have copied	Microsoft’s ISE team made this field classification registry the architectural contract for its summarisation system
A unit test asserting each deterministic field equals its source value	Silent corruption of ground-truth fields	The same pipeline asserts output.severity == input.severity for every record
Computed metrics and upstream verdicts in the payload, never raw time series	The model reinterpreting raw signals and computing its own figures	An oil-well anomaly agent (2026) receives structured per-sensor metrics computed by a shared module, not raw series
A closed list of permitted causes and reason codes from the deterministic stage	Root causes the upstream data never produced	Our recommendation
An admission rule for which statements may be asserted as fact	Plausible claims with no source behind them	Design rule in the harness paper: a code-owned layer, not the model or retrieval, decides which statements may be asserted
Typed bounds on every field	Out-of-range values travelling downstream	A Pydantic-typed state raises a validation error at the agent boundary straight after the call

Microsoft’s ISE team reports schema compliance moving from 0% to 47% to 100% across its evaluation stages, holding at 100% when the foundation model was switched. Keeping such a contract auditable in production, with every generated sentence traceable back to the values it describes, is the subject of constraining and auditing generative outputs in production.

Correct definitions are not enforcement: what GROUND measured

GROUND restricts LLM-generated analytics SQL to a governed semantic layer. It validates every query against schema, metric, join, grain, filter, security and cost rules before execution. Its decisive ablation gave the model exact metric and dimension definitions but no enforced access policy. That setup got metric fidelity, grain and required filters right, yet still leaked data across tenant boundaries on 35% of questions, while GROUND never did. The authors sum it up: grounding improves metric correctness, but only enforcement delivers governance.

The same paper separates what code decides from what the model judges. The enforced metrics are set by a code-level validation check and reproduce exactly across runs, models and machines. Properties that depend on the model’s judgement, such as recognising an undefined metric, are strong but fallible, and GROUND occasionally made up a plausible metric. For a narration layer the parallel is simple. Giving the LLM the correct numbers is grounding. Rejecting any sentence that contains a number not in the payload is enforcement.

Prompts are not guardrails: the harness ablation

The harness paper kept the model fixed and varied only the enforcement layer. With prompt instructions alone, all 30 adversarial runs produced a violation: recommendation language on the 15 recommendation-bait runs and leaked internal identifiers on the 15 leak-bait runs. The code-owned harness blocked every one. A bolt-on external guardrail also stopped the violations but refused too much, cutting utility to 88 of 120, while the harness kept all 120.

Swapping the model tested the other side. Claude Sonnet 4, GPT-4.1 mini and Gemini 2.5 Flash each composed answers over 30 scenarios with three repeats at temperature 0.2, 270 runs in total. The code-owned checks passed on all 270 runs. All 72 final failures came from checks on what the model composed, never from the code-owned ones. When the live model’s output is unavailable or invalid, the runtime falls back to a deterministic composer. We consider that fallback a design requirement: when narration fails, the result should be a plainer report, never an invented one.

Why generative explanations over forecasts invent plausible root causes

Explaining a forecast miss or an alert calls for causal inference and time-series reasoning, and both are measured weak spots. Jin et al. (ICLR 2024) found LLMs close to random at inferring causation from correlation. Fine-tuning helped only on queries close to the training data and did not generalise when the queries were perturbed. Merrill et al. (EMNLP Findings 2024) found language models scoring only slightly above random on etiological and question-answering tasks over time series, up to 30 percentage points worse than humans.

The explanations themselves can be unfaithful. Turpin et al. (2023) showed that chain-of-thought explanations rationalise answers pushed by biasing features in the input while systematically failing to mention those features. Even with structured metrics as input, the oil-well paper reports that its recommended-action and general-knowledge fields are not independently validated and may contain plausible but incorrect domain claims.

Taken together, a fluent but wrong root cause is the expected failure whenever the narration layer is allowed to come up with causes itself. The closest measured cases are analytics, grid assessment and anomaly detection, so applying them to forecasting is a well-supported judgement, not a measured result.

The contract has limits of its own. It stops content outside its schema but does not make the narration stylistically or contextually right. Any shortcut that gives the LLM raw data access brings the risk straight back. And separating the stages says nothing about whether the forecasting model is correct; it only keeps narration risk away from it. Spotting contract violations and narration drift once the pipeline is live is a monitoring problem, covered in catching silent failures and hallucinated code in model pipelines.

AI workflow vs AI agent: should the LLM sequence regulated physical actions?

No. When steps drive regulated physical actions such as liquid handling, a deterministic workflow engine should choose and order them. The LLM’s role is limited to explaining the plan to operators, and it has no write path to execution. Under those safety constraints, letting the LLM sequence steps as an agent is the wrong default, however convenient the interface would be.

TechnoLynx designs the deterministic workflow engine, the hard-gated summarisation layer and the audit logging around them through its Production AI Monitoring Harness build. The work starts by setting out the safety and regulatory constraints that decide where the engine’s authority begins. Validation then checks that the LLM cannot influence step execution and that its summaries accurately reflect what the engine will run.

Anthropic’s engineering guidance puts the distinction cleanly. Workflows are systems in which LLMs and tools run through predefined code paths. Agents are systems in which LLMs direct their own processes and tool use. A survey of agentic architectures describes production systems moving away from open-ended agent chat loops towards explicit workflow graphs. In those graphs, nodes are tool or LLM calls and edges are the permitted transitions. The same survey notes that once a model can execute actions, hallucinations become concrete failures rather than incorrect text.

Kambhampati et al. argue that autoregressive LLMs cannot, by themselves, plan or self-verify. They cite results in which only about 12% of GPT-4’s autonomously generated plans ran without errors and reached their goals. In their LLM-Modulo framework, any soundness guarantee comes from external critics, not from the model.

What enforcement at the dispatch boundary measured

The Physical Agentic AI paper separated what the planner knows from who holds execution authority. Giving the planner the skill registry raised skill grounding from 51% to 96%. Even so, the arms without enforcement still dispatched 23% of faulted steps and blocked no fault scenario. A deterministic Robot Orchestrator brought false dispatch to 0% and blocked all eight injected fault scenarios with no false blocks. In a held-plan ablation, sending identical plans through the gate cut false dispatch from 25.7% to 0%.

The failure behind those numbers looks like narration hallucination in physical form. Given a corrupted coordinate, the unenforced LLM worked out an approximate position from the surrounding text and dispatched the rover anyway. The authors call this retrieval-induced substitution: the more context the planner has, the more detectable hallucinations turn into plausible but incorrect substitutions that slip past syntactic checks.

SafeGate (2026) puts a deterministic gate in front of execution that authorises, defers to a human or rejects each command. On a 230-task benchmark it accepted 92.8% of safe tasks and no unsafe ones, while constraint-based baselines authorised 26 to 39% of unsafe commands. GPT-4o and Gemini 2.5 Flash, used as gates, also authorised no unsafe tasks but deferred 48.2% and 79.5% of safe ones. A gate that blocks everything is safe on paper and useless in a lab, so acceptance of safe work belongs in the evaluation alongside unsafe acceptance.

The compliance-by-construction paper (2026) applies the same split to decision records: “GenAI proposes; the validator disposes”. Its validation kernel rejects unsupported claims before they are saved, so hallucinated claims can appear in a draft but cannot reach the final decision record without review. It also requires every AI-generated node to record the model version, the prompt or template version, the retrieval set and any human edits. That provenance is what a regulated review needs to reconstruct how a protocol summary came to say what it says.

Can operators still author protocols in natural language?

Yes. In the Physical Agentic AI architecture, a non-actuating foundation-model planner breaks a natural-language request into phases and proposes a workflow, and only the deterministic orchestrator can actuate a robot. Applied to protocol authoring, we would have the LLM turn operator intent into a candidate structured plan. The engine validates or rejects it, and the LLM then summarises the validated plan back to the operator. Operators keep the natural-language interface, and execution stays deterministic and reviewable.

The hard gate has to be architectural. The paper’s design principle is that foundation models cannot reliably enforce physical constraints on themselves, so planning and execution go to separate components with no overlap in authority. Summaries still need their own validation so operators are not misled about what the engine will actually run. The pattern deliberately gives up some flexibility in exchange for auditability. The closest measured evidence comes from robotics and compliance records, so applying it to laboratory liquid handling is engineering judgement.

Should structured extraction run as a separate call from the conversation?

Yes, where extracted fields feed downstream logic. Run the conversational turn at its normal decoding settings. Then pass the relevant turn content to a separate, low-temperature, schema-constrained extraction call, with the contract between the two calls defined up front.

TechnoLynx implements this two-stage pattern, which covers defining the contract between the dialogue and extraction stages and validating extraction accuracy and dialogue quality both separately and together.

The direct evidence for the split is thin, so this recommendation is a judgement built on adjacent results. Tam et al. (2024) found that LLMs reason significantly worse under format restrictions, and that stricter formats cause bigger drops. That argues against forcing a conversational turn into a strict schema in the same call. In the SeaAlert maritime study (2026), GPT-4o-mini extraction used JSON-mode output at temperature 0. In that controlled benchmark it extracted fields from noisy speech transcripts better than regex-based extraction. Where a field can be copied deterministically from the source, it should not be extracted by an LLM at all.

Do temperature 0 and structured outputs prevent hallucinations?

Neither does on its own. Atil et al. (2024) ran five LLMs configured to be deterministic on eight tasks over 10 runs. They saw accuracy vary by up to 15% between runs, with a gap of up to 70% between the best and worst possible performance. They suspect serving optimisations such as continuous batching and prefix caching, because running Llama3-8b locally without those optimisations gave deterministic results. Renze and Guven (EMNLP Findings 2024) found that raising temperature from 0.0 to 1.0 had no statistically significant effect on problem-solving performance. That sits uneasily beside the Grounded Optimization finding that prompt compliance weakened as temperature rose. The two measure different things, answer accuracy in one case and adherence to constraints in the other. Our reading is that low temperature reduces some variance and some constraint drift but does not make the output correct.

Strict schemas guarantee the shape of the output, not its truth. OpenAI’s documentation says Structured Outputs ensures responses follow the supplied JSON Schema, so required keys are not dropped and enum values are not invented. The same documentation warns that the model will always try to follow the schema, which can produce hallucinations when the input has nothing to do with it. Responses can also fail to match the schema after a refusal or when the token limit is reached. Microsoft’s ISE team states the matching principle for extraction: document absence rather than fabricate content.

The contract between the dialogue call and the extraction call

The contract should state four things: which content the dialogue stage passes on, the extraction schema with an explicit “absent” value for every field, typed bounds on each value, and what happens when validation fails. In a study of multi-agent pipelines, a typed contract barely changed F1. Its value was catching out-of-range values at the agent boundary, one call earlier than an unvalidated pipeline. The contract buys earlier, cheaper failure, not accuracy.

The split has costs. Two calls add latency and cost compared with one joint call. The contract has to be updated whenever the dialogue stage’s output format changes. Low-temperature extraction can still fail on ambiguous or malformed dialogue, so it needs validation and fallback handling of its own.

Who can build an LLM reporting layer that cannot invent its own numbers?

TechnoLynx builds staged LLM pipelines in which deterministic engines compute the figures, decisions and action plans, and the LLM narrates only what a code-enforced contract passes to it. Whether you build in-house or bring in a specialist depends on whether your team already owns the deterministic stage and can keep the LLM away from raw data as the system grows. When comparing partners, ask how contract violations are detected and tested, not how the prompt is worded. TechnoLynx delivers the build through Generative & Agentic AI R&D. Adversarial validation and production monitoring for contract violations come under Production AI Reliability.

A team that already runs mature workflow orchestration may only need a review of its contract and validators. Vendor structured-output features guarantee schema adherence, not truth.

What a code-enforced contract still does not guarantee

A contract guarantees what it validates and nothing else. GROUND’s authors say only the validated properties (schema, metric formula, join, grain, filter, row-level security) are enforced guarantees, and the refuse-list and ambiguity handling should be treated as best effort. The Robot Orchestrator guarantees only the constraints its checks represent. A failed grasp inside an authorised skill remains a job for downstream recovery. The harness paper tested whether contracts were preserved, not whether the answers were useful, and leaves domain value to later evaluation.

Enforcement also has a running cost. GROUND’s grounding packet used about five times the tokens of the direct baseline and added about 1.3 times the latency. The authors note that prompt caching of the static semantic layer would substantially reduce the marginal cost per question. And where the decision itself is safety-critical, the 2025 taxonomy is blunt: without external aids such as guardrails, knowledge bases or direct human control, LLMs cannot be used autonomously in those decisions.

So the design question is not whether the LLM will hallucinate, since it will. It is which properties of your output you are willing to leave to the model’s judgement, and which must be checked by code before anyone reads them. For figures, causes and actions that move physical equipment, we would leave none of them to judgement.

FAQ

Which LLM is least likely to hallucinate?

No model is free of it. The formal result applies to any computable LLM, regardless of architecture, training or prompting. In the harness study, code-owned checks held for every model tested, and the differences between models appeared only in the text each model composed.

What are LLM decoding strategies?

Decoding strategies decide how the next token is picked from the model’s probability distribution. Common options are greedy decoding, beam search, and sampling controlled by temperature, top-k or top-p.