Technical training course

AI-Augmented Development for Senior Engineers

From Tool Usage to AI-Native Engineering Systems

  • Duration: 3 days
  • Level: Senior and leadership
  • Delivery: Instructor-led, private team delivery
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Course positioning

This programme is for senior engineers and technical leaders who must redesign software engineering practices in the age of generative AI. It connects hands-on implementation with architectural decision-making, governance, delivery workflow design, and organisational adoption.

From

AI helps me write code faster.

To

I can architect AI-augmented systems, redesign workflows, and lead AI transformation inside my engineering organisation.

Audience
Senior engineers, staff engineers, principal engineers, engineering managers, and technical CTOs.
Format
Instructor-led private team delivery in 60-90 minute learning cycles.
Value
Targets structured, measurable productivity improvement patterns rather than promising a fixed uplift.
Capstone
A practical AI integration blueprint tailored to the organisation.

Target audience

  • Senior Engineers with approximately eight or more years of experience
  • Staff Engineers
  • Principal Engineers
  • Engineering Managers
  • CTOs with a technical background

Mixed cohorts are intentional because the programme combines engineering implementation with organisational decision-making. Participants work across code, workflow design, risk, governance, and leadership proposals.

Prerequisites and pre-work

Participants should be fluent in at least one modern programming language, have experience shipping production systems, understand CI/CD and code review workflows, and be familiar with distributed systems fundamentals.

Mandatory pre-work

  • Install VS Code or another preferred IDE
  • Have access to at least one LLM provider
  • Complete a 60-minute primer covering LLM fundamentals, tokenisation, context windows, and hallucination patterns
  • Bring a real architecture problem from the participant's organisation

Organisational value

The programme is designed to produce a practical adoption playbook and a documented AI integration proposal. A 20-40% productivity uplift is treated as a measurable target or hypothesis for suitable workflows, not as a guaranteed outcome.

  • Structured productivity improvement patterns
  • A clear AI adoption playbook
  • A governance-aware integration strategy
  • Senior engineers able to mentor AI-native workflows
  • A documented AI integration proposal tailored to the organisation

Learning outcomes

Understand

  • Explain LLM mental models, context windows, token economics, deterministic outputs, and stochastic outputs.
  • Describe how AI changes software delivery lifecycle leverage points.

Apply

  • Use structured prompt engineering for refactoring, integration test generation, and domain model extraction.
  • Apply guardrails, validation workflows, prompt narrowing, and deterministic scaffolding in real codebases.

Analyse

  • Analyse architecture decomposition problems and AI-generated pull request differences.
  • Identify hallucination patterns, architectural smells, missing tests, drift, and output regressions.

Evaluate

  • Evaluate AI reliability, governance risks, data privacy, secure prompt handling, and internal model versus external API trade-offs.
  • Create and use output-quality evaluation rubrics and KPI models.

Create

  • Create a lightweight CLI-based review assistant, governance policy artefact, working integration prototype, and leadership-ready adoption proposal.
  • Design AI-native workflows, review gates, and a 90-day adoption roadmap.

Course structure

The course is delivered in 60-90 minute learning cycles. Each day contains four main sessions: two morning sessions, a 15-minute mid-morning break, a one-hour lunch, two afternoon sessions, and a 15-minute mid-afternoon break.

Three-day agenda

Day 1: AI as a Senior Engineer Multiplier

Module 1: The AI Capability Model
  • AI as a systems multiplier; LLM mental models; SDLC leverage points; context windows and token economics
  • Deterministic and stochastic outputs; AI cognitive load transfer
  • The Four Leverage Layers: Code Generation, Code Transformation, Architectural Reasoning, Workflow Automation
  • Poor prompts compared with structured prompts; legacy code refactoring; architecture decomposition
  • Structured prompt engineering lab; integration test generation; domain model extraction
  • Reflection on failure and supervision; competency evidence produced by participants
Module 2: AI Pair Programming at Scale
  • Safe AI use in real codebases; guardrail design; AI supervision models
  • Drift detection; context compression; prompt versioning
  • A supervised AI refactoring sprint; hallucination tracking; validation workflows
  • Output regression detection; prompt narrowing; deterministic scaffolding
  • Documented failure modes and mitigation patterns

Day 2: AI-Native Engineering Workflows

Module 3: AI in the Software Delivery Lifecycle
  • Requirements synthesis; test generation; code review augmentation; documentation automation
  • Measuring productivity improvement; AI-generated architecture decision records
  • AI-assisted pull request review; regression test generation
  • Building a lightweight, stack-agnostic, CLI-based review assistant
  • Reviewing pull request differences; identifying architectural smells; suggesting missing tests
  • Creating an output-quality evaluation rubric
Module 4: Reliability, Governance and Risk
  • AI reliability; enterprise guardrails; hallucination taxonomy; data privacy
  • Model drift; secure prompt handling; internal models compared with external APIs
  • Governance design for a regulated enterprise
  • Governance design for an agency serving multiple clients
  • A governance policy artefact

Day 3: Strategic Transformation and Capstone

Module 5: Designing an AI-Augmented Engineering Organisation
  • Organisation-level workflow redesign; ROI analysis; AI-native team topology
  • The changing leverage of junior and senior engineers; AI review gates
  • Knowledge-retention risks; build-versus-buy decisions
  • How AI changes expectations of senior engineers
Module 6: Capstone: AI Integration Blueprint
  • Participants work in teams to build a working prototype, integrate AI into part of the software delivery lifecycle, demonstrate measurable leverage, and define evaluation criteria.
  • Example prototypes: AI Test Generator CLI, Architecture Review Assistant, Refactoring Copilot Workflow, Documentation Synthesiser
  • Leadership proposal deliverable: business case, risk mitigation, 90-day adoption roadmap, KPI model, and cost analysis
  • Final activity: 15-minute presentation and critique panel

Course outputs

Participants leave with practical artefacts that can be reviewed, adapted, and used as the basis for internal adoption.

  • Structured prompt artefacts
  • Refactored code with tests
  • Documented AI failure modes
  • Mitigation patterns
  • A working integration prototype
  • An output evaluation rubric
  • A governance draft
  • A leadership-ready AI adoption proposal

Bring this programme to your engineering team

Eruditology offers private team delivery with customisation for your organisation's technology stack, adaptation for regulated environments, and remote or onsite delivery from the training studio model already used by the business.

Course enquiry

Send the team context, preferred delivery window, and any governance constraints that should shape the programme.

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