Question-Based
Prompts.
Extract precise information from AI with interrogative prompts. Master direct questions, multi-step chains, and structured extraction for 90%+ accuracy retrieval.
Why Questions Beat Imperatives
Question-based prompts extract precise information by structuring queries to target specific details, contexts, or analyses. Imperatives ("Summarize X") invite interpretation; questions ("What are the 3 main causes of X?") demand focused extraction, boosting precision 20-40% via natural QA training.
❌ Imperative (Vague)
"Summarize sales performance."
Gets broad, unfocused summary
✅ Question (Precise)
"What were Q3's top 3 revenue drivers with % contribution?"
Gets specific, structured data
Question Types and Applications
1. Direct Factual Extraction
Template: "What is [ENTITY] in [CONTEXT]?" Example: "What is the CEO of Tesla mentioned in this article?" Result: Pulls exact names, dates, numbers. Use case: Named entity recognition, fact verification, key info retrieval
2. Multi-Part Interrogatives
Template: "List [NUMBER] [ATTRIBUTES] from [SOURCE]." Example: "From this report, list the top 5 revenue drivers with % contributions." Result: Structured lists/tables. Use case: Comparative analysis, ranked extraction, quantitative data
3. Comparative Questions
Template: "Compare [A] vs [B] on [CRITERIA]." Example: "Compare Python vs Java for web dev on: speed, cost, ecosystem (table)." Result: Decision aids, pros/cons matrices. Use case: Vendor selection, feature comparison, trade-off analysis
4. Causal/Reasoning Queries
Template: "Why [PHENOMENON]? Explain step-by-step." Example: "Why did sales drop Q3? Analyze step-by-step from data." Result: Root cause analysis, reasoning chains. Use case: Problem diagnosis, root cause analysis, explainability
Advanced Extraction Techniques
Chain-of-Question (CoQ)
Break complex extraction into smaller sequential questions:
1. Identify all dates in text. 2. Which relate to events? 3. Extract event descriptions for those dates. Benefit: Mitigates overload, reduces hallucinations
Template Fill Extraction
Find: Person worked at [COMPANY]. Output: "[NAME] at [COMPANY]" or "Not found." Benefit: NER-like precision, minimizes noise
Few-Shot QA Calibration
Ex1: "Capital of France?" → "Paris" Ex2: "GDP of Japan?" → "$4.2T" Q: "Revenue of Apple FY24?" → ? Benefit: Calibrates nuance, sets output format
Best Practices for Effective Questions
| Pitfall | ❌ Bad Question | ✅ Good Question |
|---|---|---|
| Vague | "Info on sales" | "What drove Q4 sales growth %?" |
| Leading | "Confirm X caused Y" | "What factors contributed to Y?" |
| Compound | "Sales and costs?" | "Top 3 sales drivers? Costs breakdown?" |
| Open-Ended | "Tell about X" | "3 key benefits of X?" |
Optimization Techniques
- Be Literal: "What number?" not "Was number reported?"
- Add Constraints: "Quote exactly" / "Top 3 only" / "2-sentence answer"
- Request Verification: "Confidence? Sources? Quote text."
- Specify Format: "Table: | Col1 | Col2 |" or "JSON:" or "Numbered list:"
Domain Applications
Research/Lit Review
Question: "From abstracts: What methods for [TOPIC]? Authors/journals/year (table)."
Use case: Gap spotting, literature synthesis
Data Extraction
Question: "Extract: Invoice total, date, items (JSON). [IMAGE/TEXT]"
Use case: Document processing, structured output
Troubleshooting
Question: "Screenshot error: What line causes it? Likely fix?"
Use case: Debugging, error analysis
Financial Analysis
Question: "From earnings call: What's guidance for FY25? Direct quote?"
Use case: Earnings analysis, investor research