Viji Munuswamy — Enterprise AI Systems Architect

Key Capabilities Developed

Leverage AI for development, debugging, and system design

Build ERP-aligned features within Olivine governance constraints

Design AI-integrated workflows (not just writing code)

Operate with partial autonomy using AI systems

Master prompt engineering and iterative prompting

Build RAG systems and AI agents for ERP automation

Roadmap Phases

Install VS Code, Cursor or Windsurf; configure GitHub Copilot, ChatGPT, Postman
Prompt fundamentals: role-based, constraint-based
Using AI for code explanation and debugging
AI-assisted refactoring techniques
Analyze an existing Olivine module using AI, document architecture breakdown

Study Materials

VS Code & AI Editors

Docs: code.visualstudio.com/docs | Tutorial: cursor.com/tutorials

GitHub Copilot

Docs: docs.github.com/en/copilot | Tutorial: github.com/github/copilot-docs

Prompt Engineering

Docs: platform.openai.com/docs/guides/prompt-engineering | Tutorial: promptingguide.ai

AI Debugging Techniques

Tutorial: github.com/dair-ai/Prompt-Engineering-Guide | Deep Dive: learnprompting.org

Output Expectation

Configured development environment with AI tools
Mastered 5+ prompt patterns for code generation
Documented architecture breakdown of one existing module
Demonstrated AI-assisted debugging on real issue

Generate CRUD application using AI prompts
Refactor poor-quality code using AI suggestions
Generate comprehensive unit tests with AI

Study Materials

AI-Powered CRUD Generation

Tutorial: github.com/features/copilot | Example: github.com/microsoft/generative-ai-for-beginners

AI-Assisted Refactoring

Docs: code.visualstudio.com/docs/editor/refactoring | Guide: refactoring.guru

AI Test Generation

Jest: jestjs.io/docs/getting-started | Pytest: docs.pytest.org | Tutorial: freecodecamp.org/news/how-to-write-unit-tests-in-python

Output Expectation

Generated functional CRUD application using only AI prompts
Refactored legacy code module with AI assistance, documented improvements
Created comprehensive test suite (80%+ coverage) using AI-generated tests

Generate Node.js APIs with structured prompts
Create React/Next.js components via AI prompts
Auto-generate API documentation (Swagger/OpenAPI)
Follow menuConfig.ts structure and respect UI Registry mapping

Study Materials

Node.js API Development

Docs: expressjs.com/en/guide/routing.html | Tutorial: nodejs.dev/en/learn

React/Next.js Components

Docs: react.dev/learn/thinking-in-react | Next.js: nextjs.org/docs/app/building-your-application/components

API Documentation (Swagger/OpenAPI)

Docs: swagger.io/docs/specification/about | Tutorial: learn.openapis.org

Output Expectation

Built complete REST API using only AI-generated code
Created reusable React component library with documentation
Generated Swagger/OpenAPI spec automatically from code
Implemented feature respecting Olivine UI governance

Build AI-powered search over ERP data
Create document Q&A system with RAG
Develop "Ask ERP" internal assistant

Study Materials

RAG Systems (Retrieval-Augmented Generation)

Docs: python.langchain.com/docs/use_cases/question_answering | Tutorial: docs.llamaindex.ai

Vector Databases

FAISS: github.com/facebookresearch/faiss | Pinecone: docs.pinecone.io | Deep Dive: weaviate.io/developers/weaviate

AI Assistants & Agents

OpenAI Assistants: platform.openai.com/docs/assistants | LangChain Agents: python.langchain.com/docs/modules/agents

Output Expectation

Built semantic search over ERP dataset with sub-second latency
Created functional RAG system answering questions from company documents
Deployed internal "Ask ERP" assistant with context-aware responses

Build an AI Agent capable of ticket resolution automation
Develop intelligent code generation capabilities
Create ERP assistant workflows
Deliver mini AI layer integrated into ERP with backend, frontend, error handling, and AI validation logic

Study Materials

AI Agents & Automation Frameworks

LangGraph: langchain-ai.github.io/langgraph | AutoGen: microsoft.github.io/autogen | CrewAI: docs.crewai.com

Workflow Orchestration

Temporal: docs.temporal.io | Deep Dive: github.com/langchain-ai/langchain

Production AI Systems

Architecture: platform.openai.com/docs/guides/production-best-practices | Examples: github.com/microsoft/AI-For-Beginners

Output Expectation

Built autonomous AI agent handling ticket classification and routing
Created intelligent code generation pipeline for repetitive patterns
Deployed end-to-end ERP assistant with conversation memory
Delivered production-ready AI layer with error handling and monitoring

Prerequisites

Basic knowledge of Java, Node.js, or Python

Familiarity with React/Next.js and HTML/CSS

Understanding of REST APIs and databases

Access to AI tools (GitHub Copilot, ChatGPT)

Commitment to daily practice and weekly submissions

Engineering Mindset Shift

Before

"I write code"

After

"I design systems, AI assists execution"

Capability Maturity Model

🌱

Awareness

Basic understanding of AI tool capabilities and limitations

🌿

Assistance

Integrating AI into daily coding workflows

🌳

Augmentation

System-level integration and validated AI usage

⭐

Autonomy

AI-first system design with architectural thinking

Quality Capabilities Developed

Leverage AI for test design, debugging, and quality analysis

Transition from manual testing to automation-first QA

Design AI-assisted testing workflows

Validate AI-generated outputs and test artifacts

Test AI systems including LLMs, prompts, and datasets

Build intelligent, scalable quality systems

Roadmap Phases

Goal

Build strong testing fundamentals and introduce AI-assisted workflows.

Setup and Tools

Install VS Code, Cursor, or Windsurf
Configure GitHub Copilot or ChatGPT
Install Postman for API testing

Core Skills

SDLC, STLC, and defect lifecycle
Test design techniques including boundary value analysis, equivalence, and decision tables
Functional, regression, and exploratory testing
API testing basics
Intro to prompt engineering for QA

Hands-on Task

Take an existing feature
Write manual test cases
Use AI to improve or expand coverage
Compare manual versus AI-generated test quality

Reference Materials

Testing Fundamentals

ISTQB Foundation: istqb.org | Guru99 Testing Guide: guru99.com/software-testing.html

API Testing

Postman Docs: learning.postman.com | REST API Guide: restfulapi.net

AI Tools and Prompting

OpenAI Prompt Guide: platform.openai.com/docs/guides/prompt-engineering | Prompting Guide: promptingguide.ai | Learn Prompting: learnprompting.org

Outcome

Strong understanding of testing fundamentals
Ability to design structured test cases
Demonstrated AI-assisted test improvement
Clear comparison of manual versus AI-generated outputs

Goal

Move from manual testing to automation-first QA.

Focus

Programming basics with Python or JavaScript
UI automation with Selenium, Playwright, or Cypress
API automation
Basic SQL for validation

Reference Materials

Automation Tools

Selenium Docs: selenium.dev/documentation | Playwright Docs: playwright.dev | Cypress Docs: docs.cypress.io

Programming Basics

Python: docs.python.org/3/tutorial | JavaScript: developer.mozilla.org/en-US/docs/Web/JavaScript

Goal

Integrate QA into modern development pipelines.

Focus

Git and version control
CI/CD pipelines with GitHub Actions or Jenkins
Microservices and API testing strategy
Environment and deployment validation

Reference Materials

CI/CD

GitHub Actions: docs.github.com/en/actions | Jenkins: jenkins.io/doc

Version Control

Git Docs: git-scm.com/docs

Goal

Use AI as a co-pilot in testing workflows.

Focus

AI-generated test cases
AI-assisted bug reporting
Test data generation using AI
Prompt engineering for QA scenarios

Reference Materials

AI in Testing

Testim AI: testim.io | Katalon AI: katalon.com | Functionize: functionize.com

Prompt Engineering Advanced

DAIR Guide: github.com/dair-ai/Prompt-Engineering-Guide

Goal

Learn how to validate AI-based applications.

Focus

LLM output validation including accuracy and hallucination detection
Bias and fairness testing
Prompt testing strategies
Safety and guardrails validation
Dataset quality verification

Reference Materials

AI Evaluation Concepts

OpenAI Evals: platform.openai.com/docs/guides/evals | Hugging Face Evaluate: huggingface.co/docs/evaluate

Goal

Become a strategic AI-driven quality engineer.

Focus

AI-powered test automation
Self-healing test systems
Intelligent regression strategies
Observability with AI insights
Knowledge systems for test reuse using RAG basics

Reference Materials

Observability

Datadog: docs.datadoghq.com | Elastic (ELK): elastic.co/guide

RAG and AI Systems

LangChain Docs: docs.langchain.com | LlamaIndex: docs.llamaindex.ai

Prerequisites

Basic understanding of software testing

Familiarity with web applications

Interest in automation and AI tools

Willingness to learn programming basics

Commitment to continuous practice

QA Mindset Shift

Before

"I execute test cases"

After

"I design quality systems, AI assists execution"

Capability Maturity Model

Awareness

Basic understanding of testing and AI capabilities

Assistance

Using AI in testing workflows

Augmentation

AI-integrated quality engineering

Autonomy

AI-first quality strategy and system validation