Large Language Models and AI – Engineering

This course moves beyond AI fundamentals and into practical engineering decisions: where large language models actually fit in business processes, how to build usable workflows around them, and how to avoid spending money on AI in places where it adds complexity without enough operational value. It is designed for teams who now need to move from concept awareness into implementation thinking.

The focus is on real-world AI engineering: use-case selection, business-process integration, coding AI-driven workflows in PHP and Python, workflow orchestration in n8n, retrieval and automation patterns, self-hosted and cloud-hosted model delivery, and model selection based on practical criteria such as speed, cost, privacy, accuracy and supportability. It is aimed at teams who need grounded design judgement rather than generic AI enthusiasm.

Course purpose

Build the engineering judgement needed to select, host, integrate and operationalise AI solutions properly, with a strong focus on real business workflows, sensible model selection, cost-awareness and practical implementation approaches that are maintainable beyond the first demo.

Duration

2 days for most engineering-focused learners
Can be extended with additional labs if you want deeper build work in PHP, Python, n8n or hosted-model testing

Target audience

developers implementing AI-backed application features
automation engineers using n8n, APIs and workflow tools
solution architects evaluating model and hosting choices
technical managers responsible for AI delivery decisions
teams moving from experimentation into practical deployment

Prerequisites

Ideally learners will have:

completion of AI Foundation or equivalent understanding of LLM basics
confidence with prompts, APIs and general workflow concepts
basic familiarity with scripting, development or automation tooling
an interest in applying AI to real operational or commercial processes

This course assumes learners already understand the core ideas behind LLMs and now need to engineer useful solutions around them.

Learning outcomes

identify realistic business processes where AI can improve speed, quality or user experience
recognise situations where AI is likely to create cost or complexity without enough return
design and implement basic AI-enabled workflows in PHP, Python and n8n
choose models more intelligently based on task type, latency, privacy, cost and output quality
compare self-hosted and cloud-hosted model strategies with better operational judgement
understand how orchestration, retrieval and automation fit together in practical deployments
build more supportable and measurable AI processes for real environments
make safer early decisions about architecture, delivery model and ongoing spend

Detailed module structure

Unit 1: Real-world AI use cases and delivery thinking

Topics:

common business use cases that suit LLMs well
summarisation, classification, retrieval and assisted workflow patterns
what makes a use case operationally viable
where AI helps humans vs where it tries to replace poorly defined processes
translating an idea into an engineering requirement

Engineering framing: the best AI projects usually improve an already-understood process rather than trying to rescue a broken one with model output alone.

Unit 2: Choosing the right model for the job

Topics:

why model choice matters more than many teams expect
general-purpose vs specialist models
trade-offs between speed, quality, cost and context size
reasoning, coding, summarisation and extraction patterns
matching model capability to business need rather than hype

Unit 3: Where AI saves money and where it does not

Topics:

cost models for API, hosted and self-hosted usage
hidden costs in prompt design, review, testing and orchestration
where AI reduces human effort meaningfully
where AI adds latency, risk or supervision cost
real-world examples of good and bad fit

Commercial message: a process is not improved simply because an LLM can be inserted into it; the economics and operating model still have to make sense.

Unit 4: Applying AI to business processes

Topics:

identifying process boundaries and handoff points
human-in-the-loop vs autonomous workflow decisions
document processing, triage, support and internal-search scenarios
workflow reliability and exception handling
measuring whether the process is actually improved

Unit 5: Engineering AI workflows in PHP

Topics:

where PHP fits in AI-backed business systems
calling model APIs and handling responses
prompt construction, validation and output handling
logging, retries and guardrails in web-driven workflows
keeping PHP integrations maintainable and testable

Unit 6: Engineering AI workflows in Python

Topics:

where Python is strong in AI integration and experimentation
model and API interaction patterns
retrieval, preprocessing and orchestration awareness
scripting internal tools and data pipelines
balancing quick experimentation with supportable delivery

Unit 7: Orchestrating AI processes with n8n

Topics:

using n8n to connect models, data sources and actions
workflow branching, approvals and error paths
when low-code automation is the right fit
keeping automation observable and debuggable
avoiding fragile AI workflow designs

Unit 8: Retrieval, grounding and process reliability

Topics:

using retrieval to improve business relevance
knowledge grounding and document selection
reducing hallucination through better context handling
measuring answer usefulness instead of trusting fluency
supportability and auditability in AI-backed decisions

Unit 9: Self-hosting AI models

Topics:

why organisations self-host models
privacy, control and latency considerations
hardware, memory and throughput awareness
operational complexity of local or on-prem inference
when self-hosting is justified and when it is not

Hosting message: self-hosting can improve control and privacy, but it also turns model delivery into an infrastructure and support problem.

Unit 10: Cloud-hosted AI models and managed services

Topics:

benefits of managed AI platforms and APIs
cost, scaling and vendor considerations
speed of delivery vs control trade-offs
privacy, residency and commercial concerns
hybrid approaches combining local and cloud resources

Unit 11: Measurement, governance and operational sanity

Topics:

setting meaningful success criteria
tracking quality, latency and user outcomes
privacy, review and escalation controls
keeping costs visible and bounded
knowing when to stop or simplify an AI initiative

Unit 12: Designing supportable AI solutions

Topics:

documentation, rollback and fallback thinking
making AI workflows understandable for the next engineer
separating experimentation from production delivery
building supportable rather than impressive-looking solutions
creating a practical roadmap for advanced AI engineering

Labs

review several business scenarios and decide where AI is justified and where it is not
compare two or more model options for the same use case and justify the selection
outline a PHP or Python workflow that submits data to an LLM and validates the response
design an n8n automation that uses AI for triage, classification or summarisation
compare self-hosted and cloud-hosted deployment options for a practical business requirement
identify the operational, privacy and cost controls needed before rollout

Assessment

Engineering scenario

identify the correct AI use pattern for a real business process
select a suitable model and justify the choice against cost, quality and privacy needs
outline how the workflow would be implemented in PHP, Python or n8n
describe whether self-hosting or cloud hosting is more appropriate and why

Engineering knowledge check

Explain how AI should be applied to real business processes using appropriate model selection, sensible hosting, workflow coding in PHP, Python or n8n, and clear judgement about where AI creates measurable value versus where it simply increases cost, risk or support overhead.

Better AI use-case judgement - Better model selection - Better delivery outcomes

Built for teams who need to engineer practical AI solutions that save time, control cost and remain supportable in the real world

Training scope and tailoring

The training plan shown above is provided as a structured guide to the typical scope and direction of the course. Our training content is reviewed and refined over time, so the precise balance of modules, examples and exercises may vary when the course is delivered.

Where there are specific topics, technologies or operational outcomes that are particularly important to your team, these can normally be incorporated into the delivery plan by prior agreement. Training is not treated as a rigid, fixed package; it is adapted where appropriate to reflect the client environment, delegate experience level, group size and the objectives agreed in advance.