Course Overview

Foundation of AI-Native Product Engineering 

Instructor(s):

• Dr. Md Alamgir Kabir, Assistant Professor & Coordinator, MIS. Dept. of CSE Daffodil International University
• Ovi Shekh, AI Native Engineer Founder Of Arklab AI

Duration: 48 Hours

Mode of Delivery: Blended Learning (Technical Laboratory Coding + Continuous Hands-On Applied Architecture Sprints)

Course Description

This foundational course is designed to transform traditional deterministic software developers into hands-on AI-native product builders. Rather than simply teaching how to use AI tools, the course guides learners step-by-step through building an AI-native product or service from the ground up - covering prompt orchestration pipelines, context-window token management, document parsing, and localized data retrieval architectures. Learners move beyond basic AI chat interfaces to programmatically engineer a stable, predictable, and fully working AI-native product or service that interfaces securely with Large Language Models.

Evidence of Demand

Rapid digitization across enterprise engineering platforms in Bangladesh  and around the world has sparked an intense, immediate need for software developers who can go beyond using basic AI chat interfaces and instead build a working AI-native product or service — one that programmatically and securely interfaces with Large Language Models.

Purpose and Objectives

To help students move from simply using AI chat tools to actually building an AI-native product or service — learning the basic skills needed to create AI-powered software that works reliably and safely.

Objectives:

• Understand the basics of AI systems — how prompts work, how much text an AI model can "read" at once (context window), and how to control its behavior.
• Build an AI-native product or service — by creating a working application that reads and processes documents (parsing), and a simple RAG-based system that lets AI answer questions using a local database.
• Learn to make AI outputs reliable — by forcing the AI to respond in a fixed, predictable format (like JSON) so it works properly with other software.
• Learn to explain and present AI product ideas — through a simple requirement document (PRD) and a working demo, so others can understand what was built and why.

Course Content & Class Plan (Modules)

• Module 1: AI-First Thinking & Context Mechanics: Hands-on evaluation of context window limits, token calculation debugging, and tracking API runtime latencies.
• Module 2: AI-Native Product Idea Generation: Evaluating deterministic software boundaries vs. probabilistic AI-first product opportunities; mapping real-world problems to functional AI ideas.
• Module 3: Prompt Engineering with Tools and Functions: Writing, testing, and versioning system prompts; configuring explicit function-calling architectures for predictable JSON outputs.
• Module 4: Scraping & Parsing (AI Ready Data Platform): Writing data ingestion scripts to scrape text and parse unstructured files (PDFs, Markdown); implementing semantic document chunking.
• Module 5: RAG Models (AI Ready Data Platform): Initializing/managing open-source local vector databases (ChromaDB); generating text embeddings and implementing vector similarity search.
• Module 6: Visualization and Presentation: Designing clean user interfaces to expose semantic data metrics, retrieval histories, and model outputs.
• Module 7: Capstone Project - Idea to Pitch Deck: Consolidating validated product concepts into technical pitch decks and presenting architectural blueprints.
• Module 8: Building a PRD and Build Prompt: Writing a technical Product Requirement Document (PRD) mapped to AI system behaviors and translating it into an immutable master prompt stack.
• Module 9: Using AI Native Tools for Building Scalable Applications: Orchestrating components into an end-to-end web application using rapid prototyping libraries (Streamlit or Gradio) and deploying a live sandbox.

Practical & Field Work

• AI-Native Product Build Sprints: Continuous hands-on laboratory sessions where learners incrementally build their own AI-native product or service, module by module.
• API & System Testing: Profiling computational overhead, testing system response degradation under max token pressure, and managing developer keys while building the product.
• End-to-End Product Assembly: Orchestrating ingestion, prompt templates, and generation components into a complete, working AI-native product or service using rapid prototyping libraries..
• Local Deployment: Deploying the finished AI-native product or service as a live, functional sandbox on a local network environment.
• Final Code Defense: Timed practical presentation and defense of the built AI-native product or service, judged on database consistency, clean execution, and script structure.

Learning Outcomes

1. Build an AI-native product or service — including a document parsing/chunking pipeline and a working RAG system using an open-source local vector database.
2. Explain the fundamental architecture, capabilities and operational limits of contemporary AI systems — including context windows, tokenization, and large language model behavior — with reference to well-established principles of computer science, as a basis for engineering stable, predictable AI-integrated software.
3. Evaluate the reliability and structural performance of language-model outputs by enforcing strict JSON object structures and testing retrieval accuracy, to ensure predictable and stable software-API behavior.
4. Communicate AI-native product design decisions effectively to technical and non-technical audiences through a structured Product Requirement Document (PRD), a technical pitch deck, and a live, functional application demonstration.
5. Apply foundational ethical and safety principles — including defensive guardrails against jailbreaks and responsible AI-tool disclosure — while building an immutable master prompt stack aligned with sound AI-product governance practices

Target Audience & Requirements

Target Audience

Individuals ready to move from using AI tools to building an AI-native product or service, including:

• Software engineers, full-stack web and mobile developers, and database administrators seeking to add product-building AI skills to their toolkit.
• Senior undergraduate university students (CSE, SWE, CIS) who want practical experience building a real AI-native product or service before entering the workforce.
• AI enthusiasts with a technical background who want to move beyond prompting and start shipping functional AI products.

Entry Requirements

• Foundational proficiency in Python programming syntax (loops, dictionaries, functions, and standard object-oriented concepts) — necessary for building an AI-native product or service from scratch.
• Core understanding of client-server operations and basic web APIs, which underpin how an AI-native product or service communicates and functions.
• Minimum Age: 18 Years.

Career Pathways:

• Earns professional micro-credential credits that validate hands-on capability in building an AI-native product or service — a core, in-demand skill across Bangladesh & International software industry.
• Serves as the mandatory technical prerequisite to unlock entry into the "Advanced of AI-Native Product Engineering" course, where learners scale a single AI-native product into complex, multi-agent AI-native products and services.
• Opens pathways toward roles such as AI Product Engineer, AI-Integrated Software Developer, and Junior AI Systems Engineer.

Assessment Criteria:

•  Continuous Build Commits: 40% — incremental progress on building the AI-native product or service
•  Functional Data Ingestion & Retrieval Pipeline Test: 20% — core pipeline powering the product
•  Final Product Deployment & Code Defense: 40% — live presentation and defense of the completed, working AI-native product or service

Tools & Resources:

• Facilities & Equipment: Multimedia computer laboratory setups and persistent internet routing to support continuous building of the AI-native product or service.
• Software & Integrations: Local development IDE installations. API environment tokens, open-source local vector database, and rapid prototyping libraries- all used directly to build and deploy the AI-native product or service.
• Class Size: Maximum 25–30 participants per operational batch section, ensuring each learner receives direct instructor support while building their AI-native product or service.

Financial Information, Certification

• Tentative Course Fee: 3,000 BDT
• Certification: Earns professional micro-credential credits upon completion.