AI Landscape Quiz: Do You Know the Models That Matter?

A small business evaluates an AI tool and discovers it will fine-tune its model using the company's customer data to improve future recommendations. What is the most important consideration before agreeing?

Whether the model will be available to the business for download Whether the model improvement will be visible to the business within 30 days Whether fine-tuning on small business data produces measurably better results Whether using customer data for model training complies with the business's privacy policy and applicable data protection regulations — and whether customers consented

Which Gemini feature most directly reduces hallucination on questions about recent events?

Gemini's fine-tuning on Google's proprietary dataset Gemini's large context window Gemini Flash's lower latency Google Search grounding — responses can be anchored to live Search results

A small business owner is considering three AI marketing tools, all with similar feature sets. Which evaluation question is MOST important for long-term cost management?

Which tool has the best-designed user interface? Which tool was most recently founded? Which tool has the most integrations in their marketplace? What is the pricing at the volume I'll be using? (Many tools have low entry prices that become expensive at scale)

Which prompting technique best helps a professional use AI to identify blind spots in their own analysis?

Ask AI to score the analysis out of 10 Ask AI to list the critical assumptions the analysis relies on, then ask what would happen if those assumptions were wrong Ask AI to summarize the analysis back in its own words Ask AI to write a better version of the analysis

A company wants to connect an AI assistant to its private internal knowledge base so it can answer employee questions. Which technique is most relevant?

Fine-tuning the foundation model from scratch Retrieval-Augmented Generation (RAG) Switching to an open-source model Building a new foundation model

What is the biggest risk when an AI produces a confident-sounding analysis of your business?

AI doesn't know your actual organizational context — it analyzes the concept you described, not the specific realities of your situation AI analysis is too long — it always produces 10+ pages AI may accidentally leak your business data to competitors The AI may refuse to complete an analysis if the business is in a regulated industry

Which of these vendor claims is a RED FLAG suggesting a tool may not have meaningful AI capability?

The tool requires a free trial signup before showing pricing The vendor has raised venture capital funding The tool integrates with Zapier 'AI-powered' prominently featured in marketing but no specific explanation of what the AI does in the product

Which of these prompt template practices is MOST important for a large team to maintain quality over time?

Document expected outputs alongside each template so users know what 'good' looks like and can spot when quality has degraded Require every user to rewrite 30% of the template before use Rotate to a new AI model every quarter to keep outputs fresh Lock down templates so only administrators can use them

A healthcare company is evaluating an AI vendor's product for processing patient records. The vendor's product is built on a closed-source foundation model API. What is the primary data privacy consideration?

The foundation model will permanently memorize patient data Closed-source models automatically comply with HIPAA Patient data will be transmitted to the foundation model provider's servers The application layer encrypts data before it leaves the company

What is the primary reason Meta makes its Llama models available as open-source at no charge?

To establish Llama as the dominant open-source standard, growing Meta's AI ecosystem influence and accelerating research adoption Meta's Llama models are too weak to sell commercially Open-source is required by US government regulation for AI models Meta cannot afford to run a paid API service

A small accounting firm uses the free version of an AI chatbot to help draft client financial summaries. The summaries include client names and income figures. What is the primary data risk?

Free-tier AI tools may use inputs for model training, potentially exposing client financial data — this likely violates professional confidentiality obligations Free AI tools automatically encrypt all submitted data No risk — small accounting firms are exempt from data confidentiality regulations The AI chatbot will share client financial data with the IRS

A small law firm uses an AI document tool that stores all case materials. They are considering switching providers. What vendor-related risk should they have addressed during onboarding?

Whether case documents can be exported in standard formats — allowing migration without data loss if they switch providers Whether the new provider offers a lower price Whether the existing provider offers referral discounts Whether the AI was trained on legal documents

Why can many different AI products (e.g., a writing tool, a customer service bot, a code assistant) feel similar in capability?

Foundation models are open-source and freely copyable They may all be using the same underlying foundation model via API They all copy ChatGPT's training data AI models can only do one type of task well

A team of 50 professionals already uses Gmail, Google Docs, and Google Sheets daily. Which AI path offers the lowest adoption friction?

Claude API — requires integration by a developer ChatGPT Enterprise — requires a new platform adoption Self-hosted Llama — requires technical setup Google Gemini for Workspace — AI is embedded directly into their existing tools

What is the primary benefit of marking variables in a prompt template using brackets like [CLIENT_NAME]?

The brackets are required syntax for AI models to recognize variable values It allows the template to work across different AI provider APIs It makes the changeable elements visually obvious, so any team member can fill them in without needing to understand prompt engineering Brackets encrypt the variable so the AI doesn't see the actual client name

A manager asks an AI to recommend which of two job candidates to hire. What is the correct role for AI in this decision?

AI can help structure the evaluation criteria and surface considerations, but the hiring decision should be made by the human hiring manager AI can make the recommendation, but a human must type it into the HR system AI is too risky to use in any hiring process AI can make the final hiring decision based on the resume data provided

A small business wants to use AI for drafting responses to customer complaints that include customer names and order details. What is the simplest way to reduce privacy risk while still using AI?

Ask the AI not to store the customer's name after the session Use AI only on Tuesdays when server load is lower and data is more secure Never use AI for any customer-related communications Anonymize the data — replace the customer name with 'Customer' and describe the order generically — before sending to the AI tool

A company builds a prompt library in Notion with 30 useful templates. After 3 months, some templates produce worse results than they used to. What is the most likely cause and solution?

The team is using too many templates — reduce to fewer than 10 All templates expire after 3 months due to API policy AI model updates changed behavior — review and update templates when models are upgraded, and record a version history Notion is not a suitable storage tool for prompt templates

A strategy team wants to use AI to stress-test a business plan. Which prompt is most effective for this goal?

'Summarize this business plan in three paragraphs' 'What improvements can you make to this plan?' 'Tell me if this business plan will succeed' 'Act as a skeptical board member. What are the three riskiest assumptions in this plan, and what evidence would change your view?'

A sales team writes a different prompt every time they need to generate a follow-up email after a meeting. What is the highest-leverage improvement?

Only the most senior team members should use AI — it causes quality variance Each salesperson should develop their own unique prompting style Switch to a more powerful AI model Create a reusable prompt template with clearly marked variables for meeting date, client name, key discussion points, and next steps