Transform rough ideas into execution-ready prompts.
Everyone has access to the same AI.
Not everyone gets the same results.
The gap isn't effort. It isn't intelligence.
It's translation.
Humans think in goals.
AI thinks in structure, constraints, and precision.
That mismatch causes frustration and disappointment.
YEP sits in the middle.
A translator between you and AI.
We turn your intent into instructions AI can follow perfectly.
You bring the vision.
We handle the architecture.
AI delivers.
What was missing
- •Specific tools and examples
- •Defined report structure and sections
- •Sources for data validation
What was added
- •Verification checklist for quality
- •Hyperlink and citation requirements
- •Freshness and objectivity mandates
What you'll get
- •Scannable pricing strategies table
- •Features matrix with Yes/No marks
- •Actionable key insights bullets
Competitive Analysis Report: AI Productivity Tools
Create a concise Markdown report analyzing the top 6-8 AI productivity tools (e.g., Notion AI, Grammarly, Jasper, ClickUp AI, Mem, Otter.ai). Identify leaders via current web searches on G2, Capterra, and official sites.
Structure:
- Pricing Strategies: Table with columns: Tool, Model (freemium/tiered/usage), Tiers/Limits, Monthly/Annual Costs. Source from official pricing pages; note as-of date.
- Product Offerings: Features matrix table (rows: tools; columns: core AI capabilities like automation, integrations, writing aids). Mark Yes/No/Partial.
- Market Positioning: Bullet summaries per tool: target users, UVP (quote marketing pages), perceptual strengths.
- Key Insights: 3-5 bullets on trends, gaps, opportunities.
Use primary sources (company sites, press releases) and secondary (G2, Capterra). Hyperlink all data. Maintain objective tone. Verify data freshness; flag estimates as "Illustrative from [source]".
Verification Checklist:
- Tables scannable (<20 rows total)?
- 100% citations with live links?
- Top tools market-relevant (G2 top 10 filter)?
- Insights actionable (e.g., pricing gaps)?
- No forecasts/market size?
Why it works
AI is optimized to give you a "good enough" answer as fast as possible. That's fine for simple questions. But for real work (analysis, strategy, research) you need more than pattern-matching.
YEP prompts include verification steps, structured constraints, and explicit output requirements. This forces AI to reason carefully, not just guess quickly.
The result: answers you can actually use.
See YEP in Action
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What was missing
- •Audience and use cases undefined
- •No output format or structure
- •Lacked actionable strategies
What was added
- •Evidence requirements with citations
- •Verification checklist section
- •Prioritized prompting techniques
What you'll get
- •Structured 1200-word professional guide
- •8-10 actionable prompt strategies
- •Pre/post-response verification checklist
Create a concise professional guide titled Avoiding Hallucinations in LLMs at Work for business users like analysts and managers who use LLMs for research, reports, and decisions. Limit to 1200 words.
Structure:
- Introduction (1-2 paragraphs): Define hallucinations, highlight work risks like faulty analysis, and preview strategies.
- Key Strategies (8-10 bullets): Actionable, non-technical tips using prompts only. For each:
- Bold strategy name.
- 1-2 sentence explanation.
- Step-by-step implementation (2-4 sub-bullets).
- Prompt template in code block.
- Real-world example (e.g., financial summary, legal review).
- Evidence: Cite primary sources (OpenAI/Anthropic docs, recent arXiv/NeurIPS papers like TruthfulQA benchmarks) or reports (Forrester/Deloitte); hedge claims ("suggests 20-50% error reduction per benchmarks").
- Verification Checklist (bulleted list): 5-7 pre/post-response steps.
- Conclusion: Share success metrics (e.g., "fact-check rate drops 40-60% per studies"); advise iterating prompts on validation queries.
Prioritize Chain-of-Thought prompting, source citations, few-shot examples, and simple RAG via copy-paste docs. Use free tools only. Tailor to productivity; skip coding. Reference recent benchmarks for data currency.
Data Limitations Note: Metrics like error reductions are benchmark-based estimates; cite sources inline and hedge where proxies are used.