Applied Enterprise AI &
Spec-Driven Software Development
A rigorous, hands-on certificate program for software developers, architects, and DevOps engineers across every language and platform.
DURATION
4 Weeks · 8 Sessions
FORMAT
Live Online Weekend
CONTACT HOURS
28 Hours Total
Close the AI Trust Gap in Your Codebase
This program bridges the growing AI Trust Gap in enterprise software development. Rather than teaching superficial "vibe coding" (prompt-and-pray), this curriculum arms developers with structured, spec-driven engineering practices built around Spec Kit the industry's leading open-source toolkit for Spec-Driven Development. Learners transition from basic AI code generation to building reliable, test-driven, context-aware, and production-ready applications.
Master Spec-Driven AI Engineering
Transition from unreliable chat-based prompts to structured specifications built with Spec Kit, OpenAPI/AI schemas, and context-bound workflows that produce deterministic, enterprise-grade code.
Close the AI Quality & Trust Gap
Implement AI-assisted Test-Driven Development (TDD), reusable eval harnesses, security scanning (OWASP Top 10), automated refactoring, and root-cause debugging.
Build Context-Aware & Agentic Systems
Learn context engineering, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), structured JSON outputs, and multi-agent workflows for production applications.
Why Enroll?
While AI tool adoption among software engineers has reached record highs, enterprise developers face significant friction in day-to-day production environments.
84–90%
of developers now use AI coding assistants daily
29%
trust AI output accuracy without review
66%
say debugging AI-generated code is more time-consuming
Lack of Enterprise Context
Standard AI assistants fail when introduced to large, multi-module, legacy codebases due to missing context and schema boundaries (Atlassian Developer Experience Report).
The Shift: Context Engineering & Evals
Prompt engineering has been absorbed into everyday practice — the differentiating skill is context engineering: designing what an AI system sees on every inference call, and building eval harnesses to prove a workflow is reliable rather than lucky once. This program teaches both, alongside the spec-first discipline that Spec Kit has made a mainstream engineering standard.
Program at a Glance
4 weeks · 8 live sessions · Saturdays & Sundays · 3.5 hours per day · 28 contact hours total
Context Engineering, Spec-Driven Development & Spec Kit
7 hrs
System Specification & Flow of development to create boilerplate code
AI-Assisted TDD, Output Evaluation & Security Debugging
7 hrs
Test-First Suite, Eval Harness & Vulnerability Fixes
RAG, Enterprise Context (MCP) & API Integration
7 hrs
Local Data Search & Production API Integration
Multi-Agent Systems, CI/CD & Capstone
7 hrs
Deployed AI app with GitHub Actions
Week-by-Week Modules
Every session pairs 90–120 minutes of hands-on lab work with focused instruction — so each day ends with a usable artifact, not just slides.
Spec-Driven AI & Code Generation
Language-Agnostic Foundations
WEEK 1 · DAY 1 · SATURDAY · 3.5 Hours
Beyond "Vibe Coding": Context Engineering, Specs & Spec Kit
AI Assistants, Autonomous Agents & the Trust Gap in Modern Software Engineering — why context engineering, not prompt wording, is the core skill. (60 min)
Spec-Driven Development with Spec Kit: installing and, before code generation, creating schema boundaries and domain rules by running the flow: constitution → specify → plan → tasks → implement. (60 min)
HANDS-ON LAB · 90 min
Run the full Spec Kit workflow end-to-end: use constitution to set project principles, /specify to draft an enterprise microservice spec (DB schema, API routes, edge-case constraints), plan and tasks to produce a task breakdown, then implement to generate production boilerplate with an AI agent (like GitHub Copilot, Claude Code).
Core Deliverable: Versioned Spec Kit Artifacts (constitution.md, spec.md, plan.md, tasks.md) + Generated scaffolded boilerplate code
WEEK 1 · DAY 2 · SUNDAY · 3.5 Hours
Code Refactoring, Design Patterns & Legacy Code Strategy
Prompt & context patterns for refactoring, clean code, and design pattern implementation. (60 min)
Legacy Code Modernization: capturing "as-is" behavior as a Spec Kit specification before refactoring, translating outdated syntax, updating deprecated dependencies, and framework migration. (60 min)
HANDS-ON LAB · 90 min
Ingest an unoptimized, undocumented legacy file. Write its "as-is" behavior as a Spec Kit specification to lock in a regression contract, run an automated refactoring workflow against that spec, and generate structured architectural documentation.
Core Deliverable: Refactored & Documented Legacy Code + As-Is Spec Kit Contract
AI-Driven Testing, Security & Reliability
From Passing Tests to Provable Reliability
WEEK 2 · DAY 3 · SATURDAY · 3.5 Hours
AI-Assisted Test-Driven Development (TDD) & Output Evaluation
Test-Driven AI Workflows: generating unit tests, mocks, and stubs prior to feature implementation. (45 min)
Edge-Case & Boundary Generation: null safety, rate limits, concurrency, and error handling tests. (45 min)
Building Eval Harnesses for AI Output: measuring spec-to-code reliability across repeated runs and catching silent regressions before they reach code review. (30 min)
HANDS-ON LAB · 90 min
Given a complex business rule, use AI to write comprehensive test suites first (JUnit / Jest / PyTest), generate and refine implementation code until all tests pass, then wrap the workflow in a lightweight, reusable eval harness that reruns the pipeline and scores output consistency.
Core Deliverable: Test-First Suite + Reusable Eval Harness
WEEK 2 · DAY 4 · SUNDAY · 3.5 Hours
Automated Debugging, Performance Optimization & Security Guardrails
Root Cause Debugging: analyzing stack traces, memory dumps, and distributed log files with AI. (60 min)
AI-Powered Security Auditing: detecting SQL injection, XSS, hardcoded secrets, and unsafe dependencies. (60 min)
HANDS-ON LAB · 90 min
Diagnose and patch performance bottlenecks and security flaws in a provided intentional "buggy enterprise app" using AI diagnostics.
Core Deliverable: Vulnerability Fixes & Diagnostic Report
Enterprise Context, RAG & API Integration
Grounding AI in Real Systems
WEEK 3 · DAY 5 · SATURDAY · 3.5 Hours
Grounding AI in Enterprise Context (RAG & Vector Basics)
RAG Architecture for Engineers: indexing custom documentation, internal APIs, and enterprise codebases. (60 min)
Model Context Protocol (MCP) and dynamic context indexing. (60 min)
HANDS-ON LAB · 90 min
Index a multi-file repository into a local vector index (ChromaDB / pgvector) and query it via an AI assistant to navigate complex dependencies.
Core Deliverable: Local Codebase Search (MCP / Vector DB)
WEEK 3 · DAY 6 · SUNDAY · 3.5 Hours
Embedding GenAI Capabilities into Applications
Programmatic LLM Integration: OpenAI API or Anthropic Claude, and local open-weights execution (Ollama). (45 min)
Structured Output Enforcement: enforcing JSON schemas, function calling, and deterministic response parsing. (45 min)
Choosing the Right Model & Tool: cost vs. latency vs. quality tradeoffs, and a lightweight framework for routing between frontier and local models. (30 min)
HANDS-ON LAB · 90 min
Build an application backend feature that accepts user inputs, issues programmatic LLM calls, validates structured JSON responses against a schema, renders data in a UI, and justifies its model/tool choice against a cost-latency-quality rubric.
Core Deliverable: Production API Integration with Schema Validation
Autonomous AI Agents, CI/CD & Capstone
From Individual Workflows to Production Pipelines
WEEK 4 · DAY 7 · SATURDAY · 3.5 Hours
Multi-Step AI Agents & Workflow Automation
Fundamentals of Autonomous Agents: reasoning models, planning, tool choice, and execution loops. (60 min)
Multi-Agent Systems for Developer Workflows: orchestrating specialized agents (Code Reviewer, Security Auditor, Test Generator) that consume a Spec Kit tasks.md file as their work queue. (60 min)
HANDS-ON LAB · 90 min
Configure a multi-agent developer pipeline that reads a Spec Kit tasks.md breakdown as its backlog, using lightweight agent frameworks to automatically review pull requests and generate test coverage reports.
Core Deliverable: Multi-Agent Code Review & Testing Pipeline
WEEK 4 · DAY 8 · SUNDAY · 3.5 Hours
DevOps / CI/CD Pipelines & End-to-End Capstone
Integrating AI Quality Gates into CI/CD: automated PR validation and code smell detection. (45 min)
Corporate AI Governance: IP protection, license compliance, data privacy, prompt security, and AI-free skill verification as a guardrail against critical thinking. (45 min)
HANDS-ON LAB · 120 min
Capstone: Build, test, secure, and deploy an AI-augmented service end-to-end — spec-kit driven design (constitution → specify → plan → tasks → implement), unit tests plus an eval harness, and automated CI/CD checks.
Core Deliverable: Deployed AI App with GitHub Actions
What You Will Learn
Key Outcomes Checklist
- ✓Spec-Driven Prompt & Context Engineering with Spec Kit
- ✓AI-Assisted Test-Driven Development & Eval Harness Design
- ✓Refactoring Legacy Codebases via Re-Specification
- ✓Automated Security & Debugging (OWASP Top 10)
- ✓RAG & Vector Context Search (MCP)
- ✓Programmatic LLM Integration & Model Selection Strategy
- ✓Multi-Agent Orchestration Driven by Spec Kit Task Artifacts
- ✓DevOps & CI/CD Guardrails for AI-Generated Code
Key Technologies & Concepts
AI Tools & IDEs
Spec Kit, GitHub Copilot, Claude Code, ChatGPT, Ollama
Frameworks & SDKs
LangChain, LlamaIndex, OpenAI, Anthropic, Pydantic
Infrastructure & Vectors
ChromaDB, Model Context Protocol (MCP), GitHub Actions
Core Concepts
Spec-Driven Development (SDD), Context Engineering, Test-Driven Development (TDD), Eval Harness Design, RAG, Autonomous Agents, OWASP Security
Hands-On Labs at a Glance
Spec-Driven Code Generation with Spec Kit
Automated Legacy Code Refactoring & Re-Specification
Test-Driven AI Feature Implementation + Eval Harness
AI-Driven Security & Performance Debugging
Repository Context Indexing with Vector DBs & MCP
Structured JSON Output API Integration & Model Selection
Multi-Agent PR Review & Test Orchestration Pipeline
End-to-End Enterprise AI App Build & CI/CD Deployment
Foundational Knowledge & Prerequisites
Whom This Will Best Suit
Software Developers, Full-Stack Engineers, System Architects, DevOps Engineers, and Technical Leads across all language ecosystems (Java, C#, Python, TypeScript, Go, Rust) or platforms.
Programming Experience
1+ years of experience in at least one modern programming language (Java, C#, Python, JavaScript/TypeScript, Go, C++, or Rust).
Core Software Concepts
Basic familiarity with REST APIs, JSON formatting, Git version control, and database concepts.
Tool Access
A local development environment capable of running code, and a modern code editor (VS Code).
Frequently Asked Questions
Is this course limited to Python or JavaScript developers?+
No. Over 80% of the course concepts, design patterns, testing strategies, and architectural principles are completely language-agnostic. All labs support universal formats (REST, JSON, OpenAPI, Markdown) so Java, C#, Go, Python, TypeScript, or any programming-language engineer can apply every lesson directly to their everyday work.
How does this course differ from basic "Vibe Coding" or beginner AI tutorials?+
Beginner tutorials focus on simple chat prompts to write basic scripts ("vibe coding"). This course is built around Spec Kit and focuses on Spec-Driven Development (SDD), context engineering, Test-Driven Development (TDD), AI output evaluation, RAG context, and multi-agent pipelines to eliminate hallucinations and build enterprise-grade software.
What are the live class timings?+
Classes take place on Saturdays and Sundays for 3.5 hours each day over 4 consecutive weeks (28 live contact hours total). All sessions are recorded and made available immediately for replay.
Will I receive a formal certificate upon completion?+
Yes. Participants who complete all hands-on labs and successfully submit the final Capstone project will receive a verifiable Digital Certificate of Completion in Applied Enterprise AI Software Engineering.
Ready to Close the AI Trust Gap on Your Team?
Seats are limited to maintain a live, hands-on lab ratio. Enroll in the next cohort of Applied Enterprise AI & Spec-Driven Software Development.