"In the age of Invicta-One...
We are no longer defined by the lines of code we write, but by the magnitude of the systems we have the vision to command."
Goal of this program: Transition from "Manual Practitioner" to "AI Orchestrator" 🤖
Personal productivity and individual task automation.
Trial 1 - The "Kessel Run" (Few-Shot Prompting and Formatting)
The Logic Mindset: Participants are expected to develop probabilistic reasoning and pattern recognition skills, learning to anticipate how LLMs interpret linguistic nuances like sarcasm or conflicting sentiment markers.
The Task: You have a list of 10 disorganized customer feedback comments about your Porto office.
The Challenge:
- Few-Shotting: Provide the AI with 2 examples of how you want the data categorized (Sentiment | Department | Urgency).
- The Logic Trap: Include one comment that is sarcastic (e.g., "Oh great, another rainy day in Porto, I loved waiting 40 minutes for the bus!").
- Requirement: The AI must correctly identify the sarcasm and flag it as a "Negative" sentiment despite the word "loved."
Goal: Mastery of pattern recognition and nuance.
Trial 2 - The "Architect" Constraint (Precision and Negative Prompting)
The Logic Mindset: Participants must develop architectural foresight and deterministic reasoning. You are expected to move beyond simple troubleshooting to design a logic-gate system that can evaluate its own actions against safety protocols before execution.
The Task: Ask the AI to draft a 300-word proposal for a new project in Porto (e.g., a green tech hub in Campanhã).
The Challenge:
- You must use a Persona (e.g., "Act as a Senior Urban Planner").
- Constraint: You must use "Negative Prompting". The output cannot contain the words: smart, sustainable, innovative, synergy, or future.
Goal: Achieve a professional tone without relying on corporate clichés.
Trial 3 - The "Holocron" Synthesis (Chain-of-Thought and Data Extraction)
The Logic Mindset: Participants must develop high-order synthesis and structural reasoning. You will learn to guide the AI through a hierarchical extraction process, ensuring that complex data is not just summarized, but logically restructured for different audience personas without losing core technical integrity.
The Task: Provide the AI with a long, "messy" piece of text (like a raw transcript of a 30-minute meeting or a 5-page industry report).
The Challenge:
- Step-by-Step: Tell the AI: "First, identify the 3 biggest risks. Second, create a table of action items. Third, write a 2 sentence summary for an executive who has 10 seconds to read it."
- Variable Tone: Ask it to rewrite the final summary twice: once for a technical engineer and once for a five-year-old.
Goal: Prove the AI can maintain logic through a multi-step "Chain-of-Thought".
Creating and training custom, role-specific agents.
Managing ecosystems of autonomous agents and cross-functional AI strategy.
