AI Performance Engineering Foundations – With Implementation Guide
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Trainer: David Campbell
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Intermediate
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4-6 hours learning
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24 videos
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40 minutes of video
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105 pages of Guidance
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Certificate of Completion
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Who this course is for
Ideal participants:
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Performance Engineers and Performance Testers
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Site Reliability Engineers
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Performance Architects
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Performance Test Managers
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Test Automation Engineers responsible for Performance Testing
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Engineers responsible for analysing performance test data
Not for:
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Engineers without performance testing background
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Generic AI or prompt engineering audiences
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Tool specific or vendor centric training
What You Will Be Able to Do
Gain a clear, role-focused understanding of how AI can be applied to improve system performance and business outcomes.
Measure and Control LLM Performance Like Any System
Understand how to benchmark latency, token cost and consistency of LLM calls, and make data-driven optimisation decisions.
Improve Accuracy with Real Context Using RAG
Use retrieval pipelines to ground LLM responses in real documentation instead of guesswork, significantly reducing hallucination.
Generate Reliable Outputs with Schema Enforcement
Learn how to eliminate broken pipelines by enforcing strict, machine-readable outputs instead of relying on fragile text responses.
Engineer Trade-offs Between Cost, Latency and Accuracy
Quantify how design choices like chunk size, retrieval depth and schema strictness impact performance and cost.
Turn Inconsistent LLM Behaviour Into Deterministic Systems
Apply prompt engineering, temperature control and validation layers to move from “sometimes right” to reliable, repeatable results.
Build Production-Ready AI Pipelines, Not Just Prompts
Combine API usage, validation, RAG and performance measurement into a structured, scalable engineering workflow.
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What You Will Get
Certificate of Completion
Implementation Guidelines (105 pages)
Why this Course Stands Out
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Engineering-Focused
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Vendor‑neutral
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Implementation-driven
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Built for real performance engineering workflows
From Concept to Implementation
Go beyond theory with a structured approach to build and validate real AI-driven performance solutions.
Production-Ready Thinking
Learn how to design reliable, measurable and scalable AI systems with clear trade-offs between cost, latency and accuracy.
Curious what the course looks like?
See What’s Inside The Course
Course Content Preview
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Course Lessons
Before you begin
Basic Information About the Course
No beginner content — this is applied engineering
This is a hands-on course built for experienced performance engineers.
Basic Python, APIs and performance testing concepts are expected.
Clear prerequisites + guided setup
You’ll need Python, an API key and a working dev environment.
You’ll need Python, an API key and a working dev environment.
Start with the free “Start Here” module to set everything up and understand the learning path.
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Meet the instructor
David Campbell
David is a performance engineering practitioner focusing on complex, performance‑critical systems, including AI‑driven architectures.
His work centres on:
- system‑level performance thinking
- AI performance beyond benchmarks
- bridging experimentation and production reality
His courses and artifacts reflect hands‑on industry experience, not academic theory.
The Story of a Performance Engineering Expert Company
About Loadmagic
Loadmagic specialises in performance engineering and load testing for modern, complex systems.
The perspectives in this guide are grounded in:
- real production environments
- performance‑critical architectures
- system‑level trade‑offs beyond tooling
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Not sure where to start?
FAQ – Frequently Asked Questions
Who is this course for?
Performance engineers, QA leads and test automation engineers who already work with APIs, load testing tools and basic Python.
Do I need prior experience with AI or machine learning?
No machine learning background is required, but you should be comfortable with APIs, scripting and performance testing concepts.
What are the prerequisites?
You need basic Python knowledge, an API key (Anthropic or OpenAI) and a working development environment.
How long does the course take to complete?
The full module requires approximately 4–6 hours of focused, hands-on work (including implementation and exercises).
Is this a video course or hands-on training?
This is a hands-on engineering course. The video is only part of the experience — most of the value comes from implementation and exercises.
Can I preview the course before buying?
Yes. The “Introduction” module is available for free and includes the learning path, setup guide and expectations.
What will I actually build during the course?
You will build the foundations of an AI-powered correlation workflow, including API usage, prompt design, structured output validation and RAG-based context handling.
How technical is the course?
This is a technical, implementation-focused course. It assumes engineering experience and is not designed for beginners.
Are there any additional costs?
Yes. You will use LLM APIs during the course. The estimated cost is typically under $5 depending on usage.
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