AI-Augmented Engineering & Production LLM Systems

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About Course

👋 A welcome from your AI strategist

Experienced engineers already know how to ship software. This course shows what changes when that software includes LLMs, agents and coding assistants: design, evaluation, security, cost and operating it in production.

Work through it at your own pace, try every practice task before opening the model answer, and look for my 🏛 Architect’s Notes along the way.

Mahesh Raja Vandyala
AI Strategist and Enterprise Architect · LinkedIn

For working engineers: multiply your output with AI coding agents, and design, ship and operate production-grade LLM systems. Follow Marcus Bennett, a tech lead taking an intern’s AI prototype to production for 300 business clients, while bringing coding agents into his team without letting quality slip.

Who it’s for

Software engineers, tech leads and architects with 2+ years of experience. Take the 3 common courses first; the AI Builder course is helpful but not required.

What makes this course different

One production journey: architecture, routing, retrieval, agents, evals, security and cost, on the same system
Real engineering numbers: about 75% cost reduction through routing and caching, p95 latency 9.2s → 4.8s, model choice per step backed by evals
Production templates: coding-agent instructions file, LLM design doc, threat model checklist, cost calculator, CI eval workflow
Labs with model solutions, scenario quizzes and Architect’s Notes

What you’ll learn

Coding agents across the SDLC, and a team policy for AI-assisted development
Production LLM architecture: gateway, routing, caching, fallbacks
Evidence-based model and technique selection
Production RAG with permissions; agent design for real traffic; MCP integration
Eval pipelines, CI gates, safe rollouts and observability
LLM threat modelling, reliability engineering and cost per successful task

Format

5 modules + capstone · 21 lessons · 5 scenario quizzes · about 6 weeks at 6–8 hours a week.

Prices in examples are illustrative; always use your provider’s current pricing. Last reviewed: November 2026; reviewed quarterly.

Course Content

Module 1 — AI Coding Agents in the Software Lifecycle
Use AI assistants and coding agents across design, coding, testing, review and documentation, with the practices that keep quality and ownership high.

1.1 Meet Marcus: The Shift from Typing Code to Directing It
1.2 Working Effectively with Coding Agents
1.3 Risks and a Team Policy for AI-Assisted Development
Lab: Ship a Feature with a Coding Agent, and Measure It
Module 1 Quiz: AI Coding Agents

Module 2 — Production LLM Architecture
Reference architecture for LLM features, routing, caching and fallback patterns, and evidence-based choice of models and techniques.

Module 3 — Advanced RAG & Agentic Systems
Production-grade retrieval with permissions, agent design that survives real traffic, and enterprise integration through MCP.

Module 4 — Evals & LLMOps
Treat prompts, models and retrieval as versioned code: eval pipelines, CI gates, safe rollouts, observability and feedback loops.

Module 5 — Security, Reliability & Cost Engineering
Threat-model LLM systems, engineer for latency and uptime, and manage unit economics with cost per successful task.

Capstone — Take an LLM Feature to Production
Design, build, evaluate, secure and roll out an LLM feature in a real or realistic production system.

Student Ratings & Reviews

No Review Yet

AI-Augmented Engineering & Production LLM Systems

About Course

Who it’s for

What makes this course different

What you’ll learn

Format

Course Content

Module 1 — AI Coding Agents in the Software Lifecycle
Use AI assistants and coding agents across design, coding, testing, review and documentation, with the practices that keep quality and ownership high.

1.1 Meet Marcus: The Shift from Typing Code to Directing It

1.2 Working Effectively with Coding Agents

1.3 Risks and a Team Policy for AI-Assisted Development

Lab: Ship a Feature with a Coding Agent, and Measure It

Module 1 Quiz: AI Coding Agents

Module 2 — Production LLM Architecture
Reference architecture for LLM features, routing, caching and fallback patterns, and evidence-based choice of models and techniques.

2.1 Anatomy of a Production LLM System

2.2 Patterns: Routing, Caching, Fallbacks and Batch

2.3 Choosing Models and Techniques on Evidence

Lab: Write and Review an LLM Design Doc

Module 2 Quiz: Production LLM Architecture

Module 3 — Advanced RAG & Agentic Systems
Production-grade retrieval with permissions, agent design that survives real traffic, and enterprise integration through MCP.

3.1 Production-Grade Retrieval

3.2 Agent Design That Survives Production

3.3 Enterprise Integration Through MCP

Lab: Permission-Aware RAG with an Approval-Gated Tool

Module 3 Quiz: Advanced RAG & Agents

Module 4 — Evals & LLMOps
Treat prompts, models and retrieval as versioned code: eval pipelines, CI gates, safe rollouts, observability and feedback loops.

4.1 Building an Eval Pipeline

4.2 Versioning, Experiments and Safe Rollouts

4.3 Observability and Feedback Loops

Lab: CI Eval Gate

Module 4 Quiz: Evals & LLMOps

Module 5 — Security, Reliability & Cost Engineering
Threat-model LLM systems, engineer for latency and uptime, and manage unit economics with cost per successful task.

5.1 Threat Modelling LLM Systems

5.2 Latency and Reliability

5.3 Cost Engineering: Cost per Successful Task

Lab: Red-Team and Optimise

Module 5 Quiz: Security, Reliability & Cost

Capstone — Take an LLM Feature to Production
Design, build, evaluate, secure and roll out an LLM feature in a real or realistic production system.

Capstone Brief, Deliverables and Rubric

AI-Augmented Engineering & Production LLM Systems

About Course

Who it’s for

What makes this course different

What you’ll learn

Format

Course Content

Module 1 — AI Coding Agents in the Software Lifecycle Use AI assistants and coding agents across design, coding, testing, review and documentation, with the practices that keep quality and ownership high.

1.1 Meet Marcus: The Shift from Typing Code to Directing It

1.2 Working Effectively with Coding Agents

1.3 Risks and a Team Policy for AI-Assisted Development

Lab: Ship a Feature with a Coding Agent, and Measure It

Module 1 Quiz: AI Coding Agents

Module 2 — Production LLM Architecture Reference architecture for LLM features, routing, caching and fallback patterns, and evidence-based choice of models and techniques.

2.1 Anatomy of a Production LLM System

2.2 Patterns: Routing, Caching, Fallbacks and Batch

2.3 Choosing Models and Techniques on Evidence

Lab: Write and Review an LLM Design Doc

Module 2 Quiz: Production LLM Architecture

Module 3 — Advanced RAG & Agentic Systems Production-grade retrieval with permissions, agent design that survives real traffic, and enterprise integration through MCP.

3.1 Production-Grade Retrieval

3.2 Agent Design That Survives Production

3.3 Enterprise Integration Through MCP

Lab: Permission-Aware RAG with an Approval-Gated Tool

Module 3 Quiz: Advanced RAG & Agents

Module 4 — Evals & LLMOps Treat prompts, models and retrieval as versioned code: eval pipelines, CI gates, safe rollouts, observability and feedback loops.

4.1 Building an Eval Pipeline

4.2 Versioning, Experiments and Safe Rollouts

4.3 Observability and Feedback Loops

Lab: CI Eval Gate

Module 4 Quiz: Evals & LLMOps

Module 5 — Security, Reliability & Cost Engineering Threat-model LLM systems, engineer for latency and uptime, and manage unit economics with cost per successful task.

5.1 Threat Modelling LLM Systems

5.2 Latency and Reliability

5.3 Cost Engineering: Cost per Successful Task

Lab: Red-Team and Optimise

Module 5 Quiz: Security, Reliability & Cost

Capstone — Take an LLM Feature to Production Design, build, evaluate, secure and roll out an LLM feature in a real or realistic production system.

Capstone Brief, Deliverables and Rubric

Student Ratings & Reviews

Module 1 — AI Coding Agents in the Software Lifecycle
Use AI assistants and coding agents across design, coding, testing, review and documentation, with the practices that keep quality and ownership high.

Module 2 — Production LLM Architecture
Reference architecture for LLM features, routing, caching and fallback patterns, and evidence-based choice of models and techniques.

Module 3 — Advanced RAG & Agentic Systems
Production-grade retrieval with permissions, agent design that survives real traffic, and enterprise integration through MCP.

Module 4 — Evals & LLMOps
Treat prompts, models and retrieval as versioned code: eval pipelines, CI gates, safe rollouts, observability and feedback loops.

Module 5 — Security, Reliability & Cost Engineering
Threat-model LLM systems, engineer for latency and uptime, and manage unit economics with cost per successful task.

Capstone — Take an LLM Feature to Production
Design, build, evaluate, secure and roll out an LLM feature in a real or realistic production system.