💰 Your Token Economy
Legion Mode
~600 tokens
5 AI models debate across 4 rounds, then synthesize the best answer. This is what you came for.
Parallel Mode
~200 tokens
All 5 models answer simultaneously. You see responses side-by-side. Good for quick comparisons.
Single Mode
~40 tokens
One model, one answer. Not the best use of Legion unless you specifically need a particular model.
💡 Bottom line: If you're using Single mode, you're not using Legion's superpower. Save that for when you specifically need Claude's writing style or GPT's code generation.
🧠 How Legion Mode Works
Round 1
Initial Response
Each AI answers your question independently
Round 2
Cross-Examination
Models see and critique each other's answers
Round 3
Refinement
Models improve based on feedback
Round 4
Synthesis
Best ideas combined into one answer
The result isn't just "what Claude thinks" or "what GPT thinks" — it's the combined intelligence of five different AI architectures, stress-tested against each other.
✍️ Writing Great Questions
✅ Do This
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Give context
Your situation, constraints, what you've already tried
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Be specific with numbers
Budget, timeline, team size, metrics
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Ask for tradeoffs
"What are the pros and cons" beats "what should I do"
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Frame decisions
"Choosing between A and B given X constraints"
❌ Don't Do This
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Simple factual queries
"What year was Tesla founded?" — Google this instead
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One-word questions
"Bitcoin?" — AI needs context to help you
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Objectively correct answers
"What is 2+2?" — No debate needed here
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Small talk
"How are you?" — Save tokens for real questions
📝 Example Prompts (Steal These)
We're migrating from a monolith to microservices. Should we go with Kubernetes on GCP, ECS on AWS, or stay with managed containers on Azure? Team of 8 engineers, $400k annual cloud budget, need to maintain SOC 2 compliance. What are the tradeoffs?
Our competitor just launched an AI feature. Do we build our own, buy/integrate a vendor solution, or partner with an AI startup? We have 6 months runway before market pressure hits. Engineering team of 15, $500k budget for this initiative.
Evaluate the viability of RAG vs fine-tuning for a domain-specific enterprise AI assistant. Consider cost, latency, accuracy, and maintenance burden. Our domain is financial compliance with ~50k documents.
I have offers from a FAANG company ($220k) and a Series B startup ($160k + 0.8% equity, last valued at $50M). 10 years experience, want to be VP Engineering within 5 years. How should I evaluate this decision?
Assess the risks of acquiring a 20-person AI startup with strong tech but no revenue vs building an internal AI team over 18 months. $5M budget either way. We're a $100M ARR B2B SaaS company.
🎯 When to Use Each Mode
| Use Case | Legion | Parallel | Single |
|---|---|---|---|
| High-stakes decisions | ✓ Best | — | — |
| Complex tradeoff analysis | ✓ Best | — | — |
| Research questions | ✓ Best | Good | — |
| Comparing approaches quickly | — | ✓ Best | — |
| Creative brainstorming | — | ✓ Best | — |
| Need Claude's writing specifically | — | — | ✓ Best |
| Need GPT for code | — | — | ✓ Best |
| Testing a prompt before Legion | — | — | ✓ Best |
💡 Pro Tips
🔍 Search your history
Full-text search across all your conversations. Build a personal knowledge base of AI-vetted decisions.
📥 Export everything
Copy, download as Markdown, or share any response. Great for documentation.
⌨️ Keyboard shortcuts
Enter to send, Shift+Enter for new line. Speed up your workflow.
🔑 BYOK for power users
Use your own API keys for unlimited queries without spending tokens.
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