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Welcome to The Logical Box!
Your guide to making AI work for you.
Hey,
Andrew here from The Logical Box, where I break down AI so it’s easy to understand and even easier to use.
Unsure whether an AI answer is right? When a chatbot drafts a client proposal or a sales forecast, the wrong facts can cost sales and damage trust. These five tests catch mistakes in under two minutes and give you confidence to act.
Quick‑Grab Validation Checklist
☐ Common Sense - does it pass the gut check?
☐ Source Check - where did this come from?
☐ Consistency Check - does it stay stable?
☐ Detail Test - are there actionable specifics?
☐ External Expert - who else agrees?
Use the checklist as a desk‑side reminder while you read.
1. The Common Sense Test
AI can dazzle with fancy wording, yet still spit out claims that defy everyday logic. Your first line of defense is your own lived experience.
Why it matters
If a statement contradicts basic reality, no further validation will fix it. Catching these errors early saves you from chasing ghost data.
How to run the test
Pause for five seconds and picture the claim in real life.
Ask “Would I bet $100 this is true?” If not, push back.
Probe with a follow‑up prompt: “Explain how this works in practice.”
Spot absolutes (always, never, guaranteed). They often signal trouble.
Action Step → Mentally rehearse the scenario. If it feels impossible, flag it for revision.
Extended Example:
AI: “Most U.S. office workers eat lunch at 10 PM.”
Reality check: Office cafeterias close at 2 PM. The claim fails the test; you reject it before wasting time on source hunting.
2. The Source Check
Great answers stand on strong references. Weak ones hide their roots.
Why it matters
Citing outdated blogs or anonymous forums can inject hidden bias and inaccuracies into your work.
How to run the test
Prompt: “List each fact above with its source (title, author, date, link).”
Rate the source quality (peer‑reviewed, trade journal, blog, unknown).
Cross‑check top facts on at least one independent site.
Discard statements that lack a traceable origin.
Action Step → Spend 30 seconds verifying the most critical statistic on Google Scholar or a respected industry database.
Extended Example:
AI: “Video emails boost click‑through rates by 300 %.”
You request sources and find it cites a 2014 personal blog with no data. You replace the stat with a 2024 HubSpot benchmark showing a 65 % lift credible and current.
3. The Consistency Check
Reliable information stays stable when you ask again—or tweak the wording.
Why it matters
Large language models sometimes “hallucinate” new numbers on each run. Inconsistent outputs signal low confidence.
How to run the test
Re‑ask the same question three times, changing only minor phrasing.
Compare answers side‑by‑side for shifts in numbers or reasoning.
Note any variance >10 %. Probe the AI: “Why did your answer change?”
Lock in the version that aligns best with external data.
Action Step → Duplicate the chat tab twice, adjust two words in each question, and screenshot the three results for a quick visual scan.
Extended Example:
Run 1: “Email open rate average is 21 %.”
Run 2: “Email opens average 19 %.”
Run 3: “Average opens 55 %.”
You see a wild spike on run 3, so you discard that figure and keep the range 19‑21 %.
4. The Detail Test
Vague advice feels safe but is rarely useful. Actionable specifics reveal quality thinking.
Why it matters
Teams waste hours translating fluffy guidance into concrete tasks. Demand specificity upfront.
How to run the test
Highlight every number, name, or timeframe in the answer. Few highlights = low detail.
Ask: “Add step‑by‑step instructions with metrics and deadlines.”
Check that the outcome can be measured (e.g., “Publish three LinkedIn posts per week”).
Reject broad clichés like “engage your audience” without tactics.
Action Step → After scanning, insist on at least one measurable KPI before approving the output.
Extended Example:
Initial AI plan: “Increase social media presence.”
Revised prompt: “Provide a 30‑day calendar with daily post topics, target audience, and expected engagement rate.”
The detailed calendar passes the test and can be executed tomorrow.
5. The External Expert Test
AI is a first draft. Humans who live with the problem every day provide the final safety net.
Why it matters
Industry nuances, legal constraints, or emerging news may fall outside the model’s training data.
How to run the test
Identify one subject‑matter expert (SME) per high‑impact decision.
Summarize the AI output in ≤150 words and ask the SME for a quick thumbs‑up/thumbs‑down.
Cross‑reference with authoritative bodies (regulators, standards orgs).
Adapt based on their feedback before rolling out changes.
Action Step → Forward the AI summary with the subject line “Quick gut‑check, accurate?” to a trusted advisor.
Extended Example:
AI suggests moving all customer data to a new cloud provider. Your compliance officer flags missing GDPR clauses. You renegotiate before signing, avoiding a costly fine.
Your Turn: Put It into Action
Pick one live business question and ask your AI assistant for help.
Run the answer through all five tests, using the checklist.
Note the weak spots you catch and the time you saved fixing them upfront.
These tests act as an insurance policy against costly AI errors. They save time, build trust, and keep your projects on track.
Ready to take the next step?
I work alongside businesses to develop AI skills and systems that stay with you. Rather than just building prompts, I help you become a confident AI user who can solve real problems and no more starting from zero each time.
If you are ready for some guidance to get you or your team truly comfortable with AI tools, reach out to Andrew on LinkedIn and let's talk about what is possible.
Thanks for reading,
Andrew Keener
Founder of Keen Alliance & Your Guide at The Logical Box
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