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TNGI AI Summit '26
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Engineering & AI Dev Verified Summit Presentation 55 mins 10 secs English Developer / Intermediate
Awolola Victor Ayomide

Awolola Victor Ayomide

Lead Software Engineer, GHD

Masterclass Session Topic

Building AI Products & Prompt Engineering: Problem to Prompt to Logic to Product

A hands-on technical workshop establishing a 4-phase development roadmap (Problem -> Prompt -> Logic -> Product). Victor contrasts consumer vs. builder mindsets, details the 5 pillars of system prompting, outlines full-stack AI application architecture, and demonstrates building a grounded Study Assistant app using Google AI Studio and RAG.

Masterclass Session Recording

High Definition video archive from TNGI AI Summit '26

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Jump to Key Session Moments

4 Key Topics
00:00 Pre-Session Technical Review & 4-Phase Roadmap Overview
Play Moment
12:30 Consumer vs. Builder Mindset & The 5 Pillars of Prompting
Play Moment
28:00 System Architecture, RAG Pipeline & Security Protocols
Play Moment
45:00 Live Demo: Building a Grounded Study Assistant with Google AI Studio
Play Moment

Executive Summary

A hands-on technical workshop establishing a 4-phase development roadmap (Problem -> Prompt -> Logic -> Product). Victor contrasts consumer vs. builder mindsets, details the 5 pillars of system prompting, outlines full-stack AI application architecture, and demonstrates building a grounded Study Assistant app using Google AI Studio and RAG.

Key Quotes from Awolola Victor Ayomide

"Stop asking 'what prompts can I write?' Start asking 'what real human problem can I build a solution for?'"

— Awolola Victor Ayomide

"Logic is the bridge that prevents AI hallucinations. AI operates on algorithms and requires explicit architectural rules."

— Awolola Victor Ayomide

Speaker Notes & Concept Breakdown

Authentic session concepts, frameworks, and key principles

7 Detailed Concepts
1
4-Phase Development Pathway: Problem -> Prompt -> Logic -> Product. Never jump directly from a problem to a product without defining system prompt boundaries and backend logic.
2
Consumer vs. Builder Mindset: Consumers ask 'What prompts can I write?'. Builders ask 'What real human problem can I solve, where does the data originate, and how do I enforce data security and backend business rules?'.
3
5 Pillars of System Prompting: 1. Role (define target expertise, e.g. Senior Software Architect), 2. Context (specify environment & boundaries), 3. Task (define explicit requirements & user flow), 4. Technical Constraints (enforce tech stack e.g. Node.js/PostgreSQL), 5. Output Schema (specify strict JSON/Markdown schema formats to prevent conversational filler).
4
Full-Stack AI Application Architecture: User -> Frontend (React/Next.js UI) -> Backend API (Node.js/Express) -> AI Service Model (Gemini/Claude/GPT via API Keys) -> Database (PostgreSQL/Supabase & Vector Embeddings DB).
5
RAG & Security Protocols: Use Retrieval-Augmented Generation (RAG) to ground LLM responses in uploaded text/PDF documents. Restrict user uploads strictly to PDF formats to prevent executable script injection and malicious SQL commands.
6
Live Demo — Study Assistant App: Built using Google AI Studio. Demonstrated prompt configuration, context ingestion, output validation, and rapid low-code integration.
7
MVP Strategy: Focus on core user flows (authentication, document parsing, grounded Q&A) before adding secondary features like export/printing or billing history.

Presentation Deck & Captured Screens

High-resolution slide screenshots captured during the presentation

21 Captured Slides

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Session Resource Pack & Downloads

3 Downloads

GitHub Starter Code Repository

GitHub Repo • External Link

Access File Resource

Production Prompt Engineering Cheat Sheet

PDF Guide • 3.8 MB

Access File Resource

Vector DB & Embedding Pipeline Diagram

Architectural Blueprint • 1.5 MB

Access File Resource

About the Speaker

Awolola Victor Ayomide

Awolola Victor Ayomide

Lead Software Engineer, GHD

Lead Software Engineer at GHD with extensive expertise in full-stack architecture, Cloud APIs, Large Language Models (LLMs), and scalable web systems.

Session Specifications

  • Category: Engineering & AI Dev
  • Duration: 55 mins 10 secs
  • Language: English
  • Audience Level: Developer / Intermediate
  • Session Date: TNGI AI Summit '26

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