Steer AI from Burrito Bot to Technical Lead
Replace one-off prompting with defined skills, guardrails, chained agents, and verification steps to make powerful models deliver reliable, context-aware results instead of irrelevant brilliance.
Prompting Trap: Technical Brilliance Without Product Sense
Powerful models excel at any task—like a Chipotle bot perfectly reversing a linked list in Python—but fail as products because they lack boundaries. They invent chaotic structures, guess without clarifying, and deliver confident wrong answers (100% certainty on 10% accuracy). This creates an "AI Product Sense gap": models do what they're asked, not what's right for the context. To fix it, treat AI as a Technical Lead by shifting from vacuum prompts to steered workflows, turning raw intelligence into leveraged output.
Define Skills and Constrain the Search Space
Start by replacing vague requests (e.g., "Review this code") with repeatable Skills—constrained workflows tied to one objective, like a "Paranoid Security Reviewer" hardcoded to hunt SQL injections only. Add contextual guardrails to eliminate ambiguity:
- Persona: Specify the audience upfront (e.g., "Summarize for a VP, not an engineer") so outputs match real needs.
- Schema: Enforce structured formats to prevent invented chaos, ensuring consistent, usable responses.
These constraints collapse the model's overwhelming search space, making it predictably effective rather than brilliantly off-topic.
Chain Agents and Audit for Reliability
Single prompts yield technical outputs; reliable chains deliver AI Product Sense by breaking tasks into sub-agents:
- CEO Mode: Pressure-tests logic before coding.
- Architect: Maps Model Context Protocol (MCP) and data flows.
- QA: Launches a browser for real verification in 200ms.
Always insert verification steps to combat the "Illusion of Certainty": Force the model to flag missing data or unstated assumptions before finalizing. This ensures end-to-end reliability, not isolated excellence.
Scale with Local Tools: gstack Delivers Team-Level Output
Escape chat interfaces for local execution in tools like Claude Code, Cursor, or OpenClaw, feeding in real-time team data and production lineage. Garry Tan's open-source gstack exemplifies this: Six chained skills (/plan-ceo-review, engineering manager for architecture, paranoid reviewer, /browse QA, /ship for PRs, retro tracker) turn one person into a full team. Results: 10,000 lines of code and 100 pull requests per week, sustained over 50 days. Install takes 30 seconds, but leverage requires workflow chaining—not one-offs. Most users revert to old prompting without building this muscle, missing 10x speed.