How Founders Get 10x from the Same AI
We’re in Q4 2025 and have tried SEO + influencer outreach. Suggest five alternative acquisition channels and explain why each could work.
✅ Level 5 — Make It a Two-Way Conversation
• What it looks like: you ask the AI to probe you first, to dig into root issues and assumptions.
• What you get: tailored, high-leverage advice ➳ not canned tactics.
• Why this changes things: the AI surfaces core problems and helps you prioritize the small set of actions that matter.
Quick example:
“Before you propose tactics, ask me 3 questions about our users’ biggest friction points so we can focus on the top 20% that drives results.”
💡 Real-world outcome:
When a SaaS founder running an analytics tool switched from Level 3 to Level 5 prompting, they stopped asking:
“Write a cold email for my product.”
and instead asked:
“Before writing, ask me 3 questions about my target CTOs and their decision triggers.”
The result? They discovered their real problem wasn’t email copy — it was unclear positioning. The new prompt led to sharper messaging and a 2x increase in demo replies.
✅ Why Level 5 Is the Real Game-Changer
• The AI shifts from task-doer to strategic partner.
• It asks hard, clarifying questions that reveal underlying causes.
• It produces bespoke solutions rather than recycled templates.
✅ Practical Rules I Use (do these every time)
• Start complex ops at Level 4 at minimum.
• Tell the model to “think deeply” when you need reasoning, not lists.
• Anchor prompts to a clear timeframe (e.g., “Q3 2025”) for relevance.
• Tell the model to use past chat context or uploaded material when available.
• Finish prompts with questions ➳ ask the model to probe, not just execute.
• Always give constraints (budget, format, word count).
✅ The Common Mistake Most People Make
Levels 1–3: you’re instructing the AI to do work.
Levels 4–5: you collaborate with the AI to decide what work actually matters.
The difference isn’t clever wording ➳ it’s shifting from tasking to strategizing. Great prompt engineers think like strategists; they use prompts to sharpen thinking, not replace it.
I help SaaS founders use content and AI to grow smarter. Follow me on LinkedIn: https://www.linkedin.com/in/sonu-goswami-6209a3146/
for more hands-on ideas.
I've been noticing a lot of people underestimate how hard this is - not because of prompting skill, but because it forces them to confront whether they actually understand the problem.
I’ve been experimenting with something similar on the ops side, where instead of asking:
“Help me fix our meeting structure”
I’ll push it to first ask:
“What decisions are currently slow, unclear, or getting revisited?”
Almost every time, the issue isn’t meetings — it’s ownership or decision rights.
This really hits. Most people are still stuck treating AI like a tool instead of a thinking partner. The shift from “do this” to “help me think better about this” is where the real leverage comes in. I’ve seen the same better questions lead to better strategy, not just better outputs.
True...but the real shift isn’t mindset, it’s output.
You know you’re using AI right when it changes the problem definition, not just the answer.
Better questions don’t just improve responses they expose what was wrong in the first place (usually positioning, not tactics).
We built an open-source AI orchestration tool after struggling with multi-agent workflows
Over the last few months, while working with AI tools in real projects, we kept running into the same limitation:
Most AI assistants work well for single prompts, but once tasks become multi-step or project-level, things start breaking down — context loss, inconsistent outputs, and no clear way to understand why something happened.
We initially tried stitching things together with prompts and scripts, but it quickly became fragile.
So we built AutomatosX to solve this internally.
The idea wasn’t to build another chat interface, but to focus on orchestration — planning tasks, routing work through the right agents, cross-checking outputs, and making everything observable and repeatable.
What AutomatosX currently focuses on:
Specialized agents (full-stack, backend, security, DevOps, etc.) with task-specific behavior
Reusable workflows for things like code review, debugging, implementation, and testing
Multi-model discussions, where multiple models (Claude, Gemini, Codex, Grok) reason together and produce a synthesized result
Governance & traceability, including execution traces, guard checks, and auditability
Persistent context, so work doesn’t reset every session
A local dashboard to monitor runs, providers, and outcomes
One thing we learned quickly is that orchestration matters more than prompting once AI is used for real development work. Reliability, explainability, and repeatability become far more important than raw model capability.
AutomatosX is open-source and still evolving. If anyone is curious, the repo is on github and my profile.
I’d really appreciate feedback from others who are building or using agent-based systems:
How are you coordinating agents today?
What’s been the hardest part to make reliable?
Solid work. A few practical thoughts:
How do you handle failed steps or retries without breaking the workflow?
Persistent context is great, but memory grows fast pruning/checkpointing strategy?
Multi-model reasoning is nice, but in production, consistency beats fancy aggregation.
Dashboards are useful only if lightweight and frictionless... how’s that in practice?