Pragmatic AI for Founders
Mental models, strategy frameworks, and actionable checklists for non-technical founders and product leaders building with AI.
By the end of this series you'll be able to...
- Explain why AI outputs are probabilistic, not deterministic — and use that to design systems that are reliable, not just impressive in demos. Issue 1
- Apply the AI Strategic Fit Scorecard to any feature before building — so you stop asking "can AI do this?" and start asking "should we trust AI with this?" Issue 1
- Write prompts as structured programs, not open-ended questions — with defined input schemas, output formats, and validation steps that make behavior consistent and testable. Issue 2
- Recognize when prompting is not enough and know when to reach for fine-tuning, RAG, or a hybrid approach instead. Issue 2
- Design an agent as three layers — tools, memory, and orchestration — and build data schema before you write a single prompt. Issue 3
- Prevent the two most expensive AI failure modes — hallucination and bad retrieval — by designing your data layer first with schema, metadata, and layered guardrails. Issue 4
- Detect silent errors and drift before users do — by running a three-stage validation pipeline and tracking three drift signals on a weekly cadence. Issue 5
- Make the build vs. buy vs. embed call in under five minutes using a decision tree that starts with your data, not your preferences. Issue 6
- Calculate honest AI ROI by including failure prevention value alongside time saved and revenue enabled. Issue 6
- Evaluate any AI vendor against a 15-point scorecard tied to data layer visibility and monitoring observability. Issue 6
AI Is a Probabilistic Engine, Not a Deterministic One
AI is a probabilistic pattern matcher that requires a deterministic chassis to be reliable, and most value comes from the system around the model, not the model itself.
Prompts as Control Programs, Not Questions
Prompts are control programs that compile a deterministic interface to a probabilistic engine, and the quality of that interface determines whether your AI system is reliable or brittle.
Agents as Three-Layer Systems: Tools, Memory, and Orchestration
Agents are three-layer systems where the LLM is the decision layer, but 80% of the work is software engineering -- design the data layer first, then tools, then orchestration.
Where AI Breaks and The Data Layer Solution
AI fails predictably in two ways -- hallucination and bad retrieval -- and designing your data layer first prevents 80% of production disasters.
Silent Failures and Monitoring AI in Production
The scariest AI failures are silent -- no errors thrown, just slow degradation -- and monitoring drift is the only way to catch them before users leave.
Build vs Buy vs Embed: AI Strategy That Actually Works
Most AI strategy failures come from building when you should buy, buying vendors that can't handle your data layer, or measuring ROI without counting the disasters you prevented.