What you will be able to explain
Explain why fluent generation is not fact-checking, distinguish supported, contradicted and insufficiently evidenced claims, and choose a verification step proportionate to the stakes.
The essential path is the explanation, the five-step activity and the quiz. The presentation and technical notes are optional. You can mark the lesson complete at any time.
Start with the mechanism
A token is a piece of text defined by a tokenizer, not necessarily a word. A distribution assigns probabilities across the possible next tokens. The context is the input available for this step.
Three steps to keep in mind
- 1
Split the answer into claims
A polished paragraph may contain several independently checkable statements.
- 2
Find suitable evidence
Prefer primary, current, accessible sources that directly address each claim.
- 3
Classify support
Evidence can support, contradict, or remain insufficient for a conclusion.
Try it yourself
Follow these five steps in order. Everything in the lab runs locally; no real AI model is called.
1 · Predict
The first claim says that 42 people attended a workshop. Would a schedule giving the opening time prove that number? Predict the verdict before selecting evidence.
2 · Manipulate
- Select Case 1 and start with neither source checked. Keep the claim fixed. Select only Workshop schedule and read the verdict; the schedule describes sessions, not attendance.
- Leave the schedule selected and add only Workshop report, Attendance section. Compare the verdict before and after adding this direct passage. Uncheck that report to check that the verdict returns.
- As a separate case, select Case 2; changing cases clears the selected evidence. Read the claimed year, then select only Official invitation. Compare the exact dates, not just the fact that both mention June 18.
- Select Case 3 and read both excerpts before checking either. Add them one at a time. Ask whether registration growth during a poster campaign proves that the poster caused it.
Connect a claim to evidence
Verification workshop
Choose a case, read the prepared sources, and select only those that help assess the claim.
Claim
The report says that 42 people attended the workshop.
Does the report support the announced number?
Teaching verdict
Insufficient evidence
The selected material does not directly confirm the claim.
Limits of the workshop
- Claims and documents are fictional and prepared locally.
- The lab does not search the internet or run an LLM.
- In a real verification, a relevant source must still be authenticated, dated, and put in context.
3 · Observe
The schedule alone leaves ‘Insufficient evidence’. Adding the attendance report changes the prepared verdict to ‘Claim supported’. In Case 2 the invitation contradicts the claimed year. Case 3 stays insufficient even with both passages: a correlation and a survey do not establish the claimed causal link.
4 · Explain
In your own words, explain why adding one passage changed the first verdict but adding more passages did not establish causation in the third case. Name the next check you would make before publishing an important claim. Use speech or paper; nothing is collected.
After your own explanationCompare with one possible explanation
Evidence must address the exact claim. The attendance passage directly addresses the number, while the schedule does not. More text is not automatically stronger evidence: neither poster passage rules out alternative causes. In a real check I would authenticate and date the original source, inspect its scope and methods, and keep the conclusion ‘not verified’ when the necessary evidence is missing.
5 · Qualify
Open “Check a claim”Check the model in your head
Six short questions, each with an explanation. You can retry or skip the quiz; your best score stays on this device and is shared between languages.
No tricks, just explanations
Check your understanding
Nothing is locked by this quiz. Use mistakes to refine your explanation.
Keep these three ideas
Go deeper when you need it
The essential path is complete. Open only the resources you need; none are required to finish.
Technical detailWhat the short version leaves out
Retrieval and tools can ground an answer, but they can also retrieve irrelevant material or be interpreted incorrectly.
Useful systems expose uncertainty, separate facts from hypotheses, and make sources easy to inspect.
Training data can contain mistakes and gaps; a request can be ambiguous or based on a false premise. Missing evidence, poor retrieval or pressure to answer can contribute. ‘Hallucination’ does not by itself diagnose which cause produced an output.
Prioritize claims that affect a decision. Check source authenticity, date, scope and the precise passage. For an important published date or number, open the original; for high-stakes decisions, use authoritative sources and a qualified professional. Missing evidence means ‘not verified’, not permission to present a guess as fact.
Reusable teaching resourceOpen the eight-slide presentation
Use this as a recap or a teaching outline. Download the complete Markdown file for reuse without a network request.
Sources and further reading
References checked for this English lesson on . Publication years are listed separately. These explain the mechanisms, not a current ranking of products; real systems and documentation evolve.
- TruthfulQA: Measuring How Models Mimic Human Falsehoods — Stephanie Lin, Jacob Hilton and Owain Evans (2022) (opens a new tab)Tests whether models reproduce common human falsehoods. Supports distinguishing plausible language from truthful answers; its rates apply to the tested models and benchmark, not all current products.
- HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models — Junyi Li et al. (2023) (opens a new tab)Provides generated and human-annotated examples to study hallucination recognition, including unsupported or source-inconsistent content. It does not identify one universal cause or rate.
- SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models — Potsawee Manakul, Adian Liusie and Mark Gales (2023) (opens a new tab)Uses inconsistencies across sampled responses as an error signal in generated biographies. Agreement is not independent factual evidence, so this technique does not replace reliable sources.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — Patrick Lewis et al. (2020) (opens a new tab)Combines generation with retrieved Wikipedia passages and reports improvements over a parametric-only baseline in studied tasks. Retrieval is a source of grounding, not a guarantee of truth or correct citation.
Your local progress
Status: Not started
Progress stays on this device.