The Simple Sentence That Stops AI From Lying” presents a clear, practical walkthrough by Jannis Moore that shows how to use reasoning to dramatically improve prompts and reduce AI errors over time. The video explains why hallucinations happen, why quick patches often backfire, and includes a live breakdown of a system prompt that produced the wrong behavior.
It also teaches how to use reasoning inside user messages or system prompts, practical formats like JSON responses and chain-of-thought style reasoning, and the one simple sentence that can be added to nearly every prompt to reduce hallucinations and scope creep, helping us keep models honest. A sample system prompt and reference PDF accompany the lesson so participants can apply the methods to their projects.
The Simple Sentence That Stops AI From Lying
We want to give you one small, practical intervention that consistently reduces hallucinations and scope creep across prompts and system designs. When we add a single, short sentence to system prompts and user instructions, the model gains a clear default behavior: refuse to fabricate. That simple guardrail cuts off a common failure mode — inventing details to fill gaps — without relying on long lists of prohibitions.
Exact wording of the simple sentence to add to prompts
“If you cannot independently verify a factual claim, say ‘I don’t know’ or refuse rather than invent details.”
We recommend using this exact phrasing as-is in system prompts, and as a short reminder in user-facing templates. It is explicit, short, and unambiguous: it sets a default action (say “I don’t know” or refuse) when verifiability is absent.
Why a short, declarative sentence is effective
We find that short, declarative sentences work because they reduce ambiguity for the model and for downstream reviewers. Long negative lists or layered caveats create contradictory signals and make it easy for the model to prioritize generating an answer over following constraints. A single declarative sentence is easy to parse, harder to ignore, and simple to validate during testing. It also maps directly to a binary decision the model can make in-context: either proceed with verified content or refuse. That clarity reduces scope creep where the model starts inventing related facts to satisfy an unconstrained request.
Recommended placements: system prompt, user message, and templates
We place the sentence in three locations for layered enforcement. First, include it in the system prompt so it becomes a core behavior rule for every session. Second, echo it in the user message when the request is fact-focused to remind the model of evaluation criteria. Third, bake it into any templates or API wrappers that generate user inputs so the constraint travels with the prompt. By placing the sentence at multiple levels — system, user, and template — we create redundancy that survives prompt edits and helps observation during audits.
Why AI Hallucinates
We want to understand hallucination precisely so we can design correct countermeasures. Hallucinations are not magic; they are emergent behaviors based on how models are trained and how they generate text. When we trace the root causes, the fixes become clearer.
Technical definition of hallucination in language models
Technically, we define hallucination as the production of assertions or facts by a language model that are not supported by verifiable external evidence and that the model cannot justify from its training context. In practice, this includes invented dates, incorrect citations, fabricated quotes, or confidently stated facts that are false. The key components are confident presentation and lack of evidence or verifiability.
Root causes: training data gaps, probabilistic generation, and token-level heuristics
Hallucinations arise from several foundational causes. First, training data gaps: models are trained on large, heterogeneous corpora and may not have accurate or up-to-date information for every niche. Second, probabilistic generation: the model optimizes next-token probabilities and will often generate plausible-sounding continuations even when it lacks true knowledge. Third, token-level heuristics and decoding strategies favor fluency and coherence, which can reward producing a confident but incorrect statement over admitting uncertainty. Together these elements push models toward inventing plausible details rather than signaling uncertainty.
Behavioral triggers: ambiguous prompts, open scope, and insufficient constraints
On top of those root causes, certain prompt patterns reliably trigger hallucinations. Ambiguous prompts or questions with wide scope encourage the model to fill in missing pieces. Open-ended requests like “summarize all studies on X” without boundaries invite fabrication when the model lacks a complete dataset. Insufficient constraints — absence of structure, lack of explicit verification instructions, or missing refusal criteria — remove guardrails that would otherwise prevent the model from guessing. Recognizing these triggers helps us craft prompts that limit temptation to invent.
Why Quick Fixes Make Hallucinations Worse
We’ve seen teams attempt rapid, surface-level fixes — long blacklists, many “do not” clauses, or post-hoc filters. These quick fixes often make behavior more brittle and harder to diagnose.
Problems with stacking negative instructions and long blacklists
When we pile on negative instructions and long blacklists, the prompt becomes noisy and internally inconsistent. The model must reconcile many overlapping prohibitions, which can lead to selective compliance: it follows the most recent or most salient instruction while ignoring subtler ones. Long lists also increase prompt length and complexity, which can obfuscate the core behavioral rule we want enforced. That makes testing and reasoning about behavior much harder.
How band-aid patches create brittle behavior and unexpected side effects
Band-aid patches — quick fixes applied after an incident — often produce brittle behavior because they don’t address the underlying cause. For example, adding a blocklist of fabricated items might stop that specific failure mode, but it won’t stop the model from inventing other plausible-sounding alternatives. Patches can also create adversarial loopholes where the model follows the letter of new rules while violating their intent. Over time, we get a fragile system that breaks in new and surprising ways.
Why patching symptoms hides systemic prompt or process issues
If we treat hallucinations as a series of symptoms to patch, we miss systemic issues such as ambiguous role definitions in system prompts, mismatched data scopes, or absence of verification steps in workflows. True mitigation requires diagnosing whether the model lacks knowledge, is misinterpreting scope, or is being prompted to overreach. When we fix the symptom rather than the process, hallucination rates may appear improved temporarily but return as soon as the context shifts.
Diagnosing the Root Cause in System Prompts
To fix hallucinations reliably, we need a structured audit process for prompts and message history. We should treat the system, assistant, and user messages as a combined specification to debug.
How to audit system, assistant, and user message history
We audit by replaying the conversation with explicit checks: identify the system instructions, catalog assistant behaviors, and examine user requests for ambiguity. We look for conflicting instructions across messages, hidden defaults that instruct the model to be creative, and missing verification steps. We also run controlled tests where we vary one element at a time (e.g., remove a line from the system prompt) to see how behavior changes. Logging and versioning prompt changes are crucial to correlate edits with outcomes.
Common misconfigurations that lead to wrong behavior
Common misconfigurations include vague role definitions (“You are helpful and creative”), absence of refusal criteria, asking for both creativity and strict factual accuracy without prioritization, and embedding outdated knowledge as if it were authoritative. Another frequent error is not constraining the model’s assumed knowledge cutoff — leaving it to guess temporal context on time-sensitive queries. Identifying these misconfigurations gives us clear levers to flip.
Distinguishing between knowledge errors, scope creep, and instruction misinterpretation
We must separate three distinct problems. Knowledge errors occur when the model lacks correct data. Scope creep is when the model expands the request beyond intended limits (e.g., inventing background). Instruction misinterpretation arises when the model misunderstands how to prioritize instructions. Our audit process aims to reproduce the error under controlled conditions and then vary whether additional context, constraints, or data access resolves it. If providing a verified source or schema fixes it, it’s likely a knowledge issue; if clarifying boundaries prevents excess detail, it was scope creep; if changing phrasing changes compliance, we had misinterpretation.
Live Breakdown of a Real System Prompt
We want to learn from real failures, so we present an anonymized, representative system prompt that produced incorrect answers, then walk through diagnosis and fixes.
Presentation of an anonymized real prompt that produced incorrect answers
Here is an anonymized example we observed: “You are an expert assistant. Answer user questions thoroughly and provide helpful context. When asked for facts, be concise but include supporting examples. If unsure, make reasonable assumptions to help the user.” This prompt asked the model to both be concise and to “make reasonable assumptions” when unsure.
Step-by-step diagnosis: where the logic and boundaries failed
We diagnose this prompt by identifying conflicting directives. “Make reasonable assumptions” directly encourages fabrication when the model lacks facts. The combination of “provide helpful context” and “be concise” encourages adding invented supporting examples rather than saying “I don’t know.” We reproduced the failure by asking a time-sensitive fact; the model invented a plausible date and citation. The root cause was an instruction rewarding helpfulness and assumptions without a refusal or verification clause.
Concrete edits that fixed the behavior and why they worked
We made three concrete edits: removed “make reasonable assumptions,” added our simple sentence (“If you cannot independently verify a factual claim, say ‘I don’t know’ or refuse rather than invent details.”), and added a brief schema requirement for factual responses (a “source” field when available, otherwise a refusal code). These changes removed the incentive to invent, provided a clear default refusal action, and structured outputs for easier validation. After edits, the model either cited verifiable sources or explicitly refused, eliminating the confident fabrications.
Using Reasoning Inside Prompts
We encourage using reasoning cues carefully to let models check themselves without triggering chain-of-thought disclosures. There are patterns that improve accuracy without exposing internal latent chains.
When to ask the model to ‘think step-by-step’ versus provide a concise result
We ask the model to “think step-by-step” during development, debugging, or when dealing with complex reasoning tasks that benefit from intermediate verification. For production-facing answers, we prefer concise results accompanied by a brief verification summary or explicit confidence level. Step-by-step prompts increase transparency and help us find logic errors, but they may produce private reasoning content that we do not want surfaced in user-facing outputs.
Embedding lightweight reasoning instructions that avoid verbosity
We can embed lightweight reasoning by instructing the model to perform a short internal checklist: verify sources, confirm date ranges, and check for contradictions. For example: “Before answering, check up to three authoritative sources in context; if none are verifiable, refuse.” This type of instruction triggers internal verification without demanding full chain-of-thought exposition. It balances accuracy with brevity.
Balancing useful internal reasoning with risks of exposing chain-of-thought
We must be mindful of the trade-off: internal chain-of-thought can reveal sensitive reasoning patterns and increase attack surfaces. In production, we avoid asking the model to expose raw reasoning. Instead, we request a compact justification or a confidence statement derived from internal checks. During development, we temporarily enable detailed step-by-step traces to diagnose failures, then distill the resulting rules into the system prompt and schema for production use.
The One Simple Sentence
Now we return to the core intervention and explain how it works and how to adapt it.
The one-sentence formulation and plain-language explanation of its intent
The one-sentence formulation we recommend is: “If you cannot independently verify a factual claim, say ‘I don’t know’ or refuse rather than invent details.” Plainly, the sentence tells the model to prefer abstention over invention when accuracy is uncertain. Its intent is to replace plausible fabrication with explicit uncertainty, making downstream workflows and human reviewers more reliable.
Template variations tailored for fact-based answers, opinion boundaries, and data-limited domains
We provide small template variations for different contexts:
- Fact-based answers: “If you cannot independently verify a factual claim from reliable sources or provided data, say ‘I don’t know’ or refuse rather than invent details.”
- Opinion or creative tasks: “For opinions or creative content, indicate when you are speculating; do not present speculation as fact.”
- Data-limited domains (e.g., emerging events): “For time-sensitive or emerging topics beyond our verified data, state the last verified date and refuse to invent newer facts.”
These variants preserve the core refusal behavior while tailoring language to domain expectations.
Mechanisms by which this sentence reduces hallucination and scope creep
The sentence reduces hallucination by creating a clear cost for invention — refusal becomes the default and is easier to test. It reduces scope creep by limiting the model’s license to fill gaps: instead of inventing background or assumptions, the model must either request clarification or refuse. This nudges workflows toward defensible behavior and makes downstream validation simpler.
Practical Methods to Enforce Reliable Outputs
We combine the sentence with structural and tooling measures to ensure consistent, verifiable outputs.
JSON response formatting and enforced schemas to reduce ambiguity
We enforce JSON response formats with a strict schema for fields such as “answer”, “sources”, “confidence”, and “refusal_reason”. Structured outputs make it easier to validate completeness and enforce refusal modes programmatically. If the model cannot populate required fields with verifiable values, the schema should allow a controlled refusal path rather than accepting free text.
Using explicit field-level validation and schema checks as a guardrail
We implement automated schema checks that validate types, required fields, and allowed values. For instance, “sources” should be an array of verifiable citations, or null with “refusal_reason” set. Field-level checks can run prior to returning content to users, enabling automated rejection or escalation when the model indicates uncertainty or fails validation.
Designing explicit refusal modes and safe fallback responses
We design explicit refusal modes: short, standardized statements like “I don’t know — unable to verify” or context-specific fallbacks such as “I cannot confirm that from available data; would you like me to search or clarify?” Standardized refusals avoid confusing users and support downstream metrics. We also design escalation flows: if the model refuses, the system can route the query for a human review or an external fact-check.
Chain-of-Thought and Structured Reasoning Techniques
We use chain-of-thought selectively to improve model accuracy while minimizing exposure of raw internal reasoning.
Prompt patterns that request intermediate steps without revealing private reasoning
We can request structured intermediate outputs such as “list the three key facts you used to derive the answer” instead of the full reasoning trace. Another pattern is “provide a one-line summary of your verification steps” which gives a compact proof without exposing thought chains. These patterns provide transparency while protecting sensitive internal content.
Socratic and decomposition techniques to force verification of facts
We use Socratic prompting by asking the model to decompose a question into sub-questions and answer each with an explicit source field. For example: “Break this claim into verifiable components, verify each component from context, and then provide a final answer only if all components are verified.” This decomposition ensures each piece is checked and prevents broad unsupported assertions.
When to use chain-of-thought prompts in development vs production
In development and testing, we use full chain-of-thought traces to debug and understand failure modes. These traces reveal where the model invents steps and help us refine system instructions. In production, we avoid exposing full chains; instead we use distilled verification outputs, confidence scores, or compact rationales derived from internal chains-of-thought.
Conclusion
We believe a single, well-placed sentence combined with structured reasoning and output formats dramatically reduces hallucinations.
Concise recap of why a single sentence, paired with reasoning and structure, reduces AI lying
A short declarative sentence creates a clear default: prefer refusal to invention. When paired with lightweight reasoning instructions, enforced schemas, and refusal modes, it constrains the model’s incentive to fabricate and makes verification practical. This approach addresses the behavioral root of hallucination rather than patching surface symptoms.
Practical next steps: implement the sentence, add JSON schemas, and run targeted tests
We recommend three immediate actions: (1) insert the exact sentence into system prompts and templates, (2) design and enforce JSON schemas with explicit fields for sources and refusal reasons, and (3) run targeted A/B tests and adversarial prompts to validate that the system refuses appropriately instead of fabricating. Log failures and iterate on prompt wording and schema rules until behavior is consistent.
Pointers for continued learning: sample prompts, community links, and iterative evaluation best practices
For continued learning, we suggest maintaining a library of sample prompts and failure cases, running regular prompt audits, and sharing anonymized case studies with peers for feedback. Build a small test harness that submits edge-case queries, records model responses, and tracks hallucination metrics over time. Iterative evaluation — small, frequent tests and prompt adjustments — will keep the system robust as requirements and data evolve.
We’re here to help if you want us to apply these steps to a specific system prompt or run a live audit of your prompts and schemas.
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