AI-Augmented
C++ Development

On-site corporate training · 2 days · On your code

We developed this method building critical systems. Now we pass it on to your teams.

Request a demo View the syllabus

The problem

If your teams use AI without a shared method, the results will most likely be inconsistent — and quality will depend on each individual.

AI as autocomplete

Most developers use AI to complete code. They get local suggestions, with no consistency with the project's overall architecture.

Tests that test nothing

When the same agent writes the code and the tests, the tests validate what was coded — not what was requested. Bugs slip through unnoticed.

Code that needs full review

Without a method, AI produces more code — but a senior must verify everything. The productivity gain is cancelled out by review time.

Our method

Four complementary layers, developed and proven on critical systems.

1

Context engineering

A structured document that gives the AI a complete understanding of the project — architecture, constraints, lessons learned. The AI respects your choices from the first interaction.

2

Architectural thinking

AI as a thinking partner — formulate the goal, express uncertainties, explore alternatives. Iterate until you reach a solution that passes quality criteria.

3

BMAD orchestration

10 specialized agents with strict scopes. QA has no access to code. Dev cannot modify tests. Structural separation, not conventional.

4

TDD-first with AI

Tests are written before the code, by an agent that has never seen the implementation. PASS/FAIL quality gates before each merge.

Measured results

÷3
Time per feature
x5
Test coverage
÷5
Regressions
49
Standardized projects

What AI unlocks for your C++ developers

Techniques usually reserved for experts, made accessible through the method.

Lock-free programming

Advanced atomics, CAS, memory ordering — without the usual subtle bugs.

SIMD optimization

SSE/AVX intrinsics with automatic scalar fallback. 2-4x speedup.

Template metaprogramming

C++20 concepts, SFINAE, constexpr — without cryptic compilation errors.

Advanced multi-threading

Triple buffering, thread pools, proven producer-consumer patterns.

Hardware integration

USB, serial, MIDI, DAC — AI knows the SDKs even when vendor documentation is poor.

Rigorous benchmarking

Complete framework: warmup, statistics, baseline/optimized comparisons, throughput.

C++17 / 20 / 23 migration

Modern idioms (C++17, 20, 23) applied file by file, without regression.

Network protocols

Serialization, endianness, parser + generated conformance tests.

Syllabus — 2 days / 14 hours

50% theory / 50% hands-on workshops on participants' real code.

DAY 1 Foundations

1

Context engineering and assisted prompts

3h30 — Morning
  • The context document: the 10 components that change everything
  • Compaction: less text, more signal
  • Meta-prompting: AI that writes your prompts
  • Workshop: structuring the context of your own project
2

Architectural thinking and operational AI

3h30 — Afternoon
  • The 4-step method: goal, uncertainties, unknowns, iteration
  • AI as an environment assistant: installation, diagnostics, tool selection
  • Doing with AI what you don't know how to do: SIMD, GPU, lock-free...
  • Workshop: architectural resolution + environment diagnostics

DAY 2 Orchestration and quality

3

BMAD orchestration

3h30 — Morning
  • 10 specialized agents: Analyst, Architect, QA, Dev, PO...
  • Brownfield workflow: analysis → architecture → tests → code → validation
  • Durable artifacts: PRD, epics, stories versioned in Markdown
  • Workshop: run a complete BMAD workflow
4

TDD-first with AI

3h30 — Afternoon
  • The self-validation bias: why AI that codes and tests produces complacent tests
  • Red-Green-Refactor with separate agents
  • Quality gates: PASS, CONCERNS, FAIL, WAIVED
  • Workshop: complete TDD cycle — QA → Dev → PO

What each participant leaves with

📄

Context document

Structured, versionable, ready for the repo

🛠

Meta-prompting techniques

Reformulation, clarification, iteration, templates

🧱

Operational AI reflexes

Installation, diagnostics, tool selection, domains beyond expertise

📑

Documented ADR

Architectural decision produced with AI

📂

BMAD workflow

PRD, epic and stories ready to use

✅

Complete TDD-first story

Tests + code + quality gate

After the training, your teams know how to learn on their own

AI becomes their first reflex for exploring an unknown domain — before looking for specialized training, a consultant, or documentation.

Result: continuous, self-directed skill growth, at the pace of the project.

What this training does not promise

  • AI does not replace expertise — it accelerates exploration, not judgment
  • Generated code must be reviewed, tested, and validated
  • Security, cryptography, compliance: the expert remains indispensable
  • The x3-4 productivity gain is measured on structured tasks — gains vary

Logistics

Audience and prerequisites

  • C++ developers (intermediate to senior)
  • C++ Tech Leads and software architects
  • R&D managers structuring AI usage
  • No prior AI knowledge required

Format

  • On-site or remote corporate training
  • 6 to 12 participants per session
  • Participants' Windows laptops (preparation guide provided)
  • Digital materials and templates included
  • Post-training follow-up: 1-hour video call at D+30

2-day training

3,500 € excl. tax

2-day on-site session — up to 6 participants included

+ 350 € excl. tax per additional participant

From 7 to 12 participants maximum

Request a quote

Confidentiality constraints?

We also work in air-gapped mode — local LLMs only, zero data sent to the cloud. The method is designed for environments where code does not leave the premises.

30 minutes to see the difference

Request a live demo — no slides, a real use case on actual C++ code.

Schedule a demo