AI & LLMs in practice
Artificial intelligence has gone from a specialist topic to a core skill for everyone. Whether you are non-technical or already comfortable, the stakes are the same: knowing what LLMs can and cannot do, working with them reliably, and turning them into real outcomes rather than novelty. This section helps you build genuine fluency: from the foundations of how LLMs work and prompt engineering, to using AI in daily work, structuring real projects, and mastering the specifics of ChatGPT, Claude, and Gemini. It also covers the parts people skip at their peril: hallucinations, privacy, verification, and responsible use. The goal is practical capability you can apply at work and, if you want, the level expected by certifications like Google AI Essentials, Anthropic's Claude certification, and IBM's Generative AI Engineering.
Human oversight in agent workflows: a practical playbook
As AI agents take on multi-step, consequential work inside real business processes, the question of when and how humans intervene has become a design problem, not a policy one. This playbook gives you a concrete sequence for building oversight into agent workflows before something expensive goes wrong.
How Klarna rewired its support operations with disciplined prompt engineering
Klarna's AI deployment in customer support became one of the most cited cases of LLMs producing measurable operational results. The prompt discipline behind it offers concrete lessons that transfer well beyond fintech.
Reasoning models and when to use them: the hype is ahead of the practice
Reasoning models like OpenAI's o3 and Google's Gemini 2.0 Flash Thinking have captured attention by visibly "thinking through" problems before answering. The consensus says to use them everywhere you need accuracy, but that prescription is wrong in ways that will cost you money and slow your teams down.
The spreadsheet that embarrassed a CFO and changed how we measure AI
A major retailer celebrated millions in projected AI savings, then watched the number quietly shrink to almost nothing once someone counted the full cost. That moment, repeated across industries throughout the early 2020s, explains why measuring AI returns remains the most underrated skill in enterprise technology.
The EU AI Act for non-lawyers: a practical compliance playbook
The EU AI Act is now producing real obligations for companies deploying AI in Europe, and ignorance of the legal text is not a defence your board will accept. This playbook gives you a concrete sequence of steps to assess your exposure, assign ownership, and take action before regulators come looking.
Evaluating AI apps before you trust them: a practical playbook
Most teams adopt AI applications based on demos and vendor promises, then discover the gaps only after deploying them in production. This playbook gives you a structured sequence to test what actually matters before you commit budget, data, or workflows to any AI tool.
Grounding AI in your company's knowledge: a practical playbook
Generic AI gives generic answers. This playbook shows you how to connect large language models to your organisation's own data so that every response is accurate, specific, and actually useful.
Human oversight in agent workflows: what it actually means to stay in control
As AI agents take on multi-step tasks with real consequences, the question of where humans intervene has become a design problem, not a policy slogan. This article unpacks the mechanics of oversight in agent workflows and explains when to tighten or loosen human control.
The one AI skill that outlasts every tool upgrade
Most AI tools you're using today will look different or obsolete within two years. The professionals who stay effective aren't the ones who memorize features, they're the ones who've learned to think in terms of problems, constraints, and outputs.
The lawyer who stopped re-explaining herself to ChatGPT
A corporate lawyer's frustration with AI tools that forgot everything between sessions quietly pushed a wave of professionals toward a different way of working. The shift from treating AI as a one-shot tool to giving it persistent context is one of the most underappreciated productivity changes of the past two years.
Open vs closed AI models: why the obvious choice keeps being wrong
Most organizations pick their AI model deployment strategy based on a simple story: open source is flexible and cheap, closed APIs are powerful and fast. That story leaves out the parts that actually determine whether a deployment succeeds or fails.
Turning a repeated task into an AI workflow: a practical playbook
Most professionals waste hours each week on tasks that follow the same pattern every time. This playbook shows you how to identify those tasks, convert them into structured AI workflows, and make the output reliable enough to actually use.
Reasoning models: a practical playbook for knowing when to use them
Not every task benefits from a reasoning model, and using one indiscriminately wastes time, money, and attention. This playbook gives you a concrete decision process for matching the right model type to the right problem.
Where AI bias comes from and how to spot it before it costs you
AI bias is not a glitch or an edge case. It is a structural feature of how models are built, and understanding its origins is the first step to catching it before it damages a decision, a product, or a reputation.
Giving AI the right context, not more context
Most professionals assume that longer, more detailed prompts produce better AI outputs. The real skill is something narrower: identifying which specific context actually changes the answer, and leaving everything else out.
RAG explained without the jargon: a practical playbook
Most LLMs confidently answer questions using knowledge that stopped updating months or years ago. RAG fixes that, and this playbook shows you exactly how to build it without getting lost in the technical weeds.
Where AI agents help and where they break: lessons from Klarna
Klarna ran one of the most cited enterprise deployments of AI agents in financial services, and the results were genuinely mixed. Here is what actually happened, what the numbers mean, and what any organization should take from it before committing to agent-based automation.
Vibe coding for non-engineers: the hype is real, but the risk is being misread
Coding assistants like GitHub Copilot, Cursor, and Claude have made it genuinely possible for non-engineers to build working software. But the dominant narrative around "vibe coding" is flattening a more complicated reality that professionals need to understand before betting on it.
How JPMorgan Chase built human oversight into its AI agent workflows
JPMorgan Chase deployed AI agents across legal review and trading operations, then discovered that automation without structured human checkpoints created compliance exposure it hadn't anticipated. The decisions they made to redesign those workflows offer a concrete template for any organization running agents at scale.
What actually happens to your business data when it enters an AI model
Sending a contract, a customer list, or internal financials into an AI tool feels like using a search engine. It is not, and the distinction carries real legal and competitive consequences.
Multimodal AI at work: a practical playbook for text, image, voice, and video
Most professionals are still treating multimodal AI as a novelty rather than a daily workflow tool. This playbook shows you how to combine text, image, voice, and video capabilities into concrete business tasks, starting this week.
The spreadsheet rebellion that taught us how to roll out new tools to teams
The challenge of getting an entire team to actually use a new technology is older than AI by several decades. The story of how organizations learned to do it well starts in a place almost no one remembers: a corporate fight over spreadsheets.
The Model Context Protocol: how AI actually connects to the world outside its context window
Most AI assistants are islands. The Model Context Protocol is the specification that turns them into networked systems, and understanding how it works changes what you can realistically build or demand from AI in your organisation.
Fine-tuning vs RAG: how to choose the right approach for your use case
Fine-tuning and retrieval-augmented generation solve different problems, and confusing the two leads to expensive mistakes. This article explains the mechanics of each approach and gives you a practical framework for choosing between them.
Multimodal AI explained: what it means when a model can see, hear, and read at once
Multimodal AI lets a single model process text, images, audio, and video together rather than treating each as a separate problem. Understanding how that works, and where it breaks down, changes how you design AI-assisted workflows.
Human oversight in AI agent workflows: what it actually means to stay in control
As AI agents take on multi-step tasks autonomously, the question of when and how humans intervene has become one of the most consequential design decisions in enterprise AI. This article breaks down the mechanics of oversight in agentic systems and the real tradeoffs involved.
An AI usage policy your team will actually follow
Most AI policies gather dust because they read like legal disclaimers rather than working tools. This playbook shows you how to build one your team treats as a genuine guide, not a compliance checkbox.
Measuring real ROI from AI adoption: a playbook that actually works
Most companies deploying AI in 2026 cannot tell you whether it's paying off, because they're measuring the wrong things at the wrong time. This playbook gives you a concrete sequence to build an ROI framework that holds up to CFO scrutiny.
The prompt is the product: why most professionals are leaving AI performance on the table
Most professionals using AI tools in 2026 treat prompting as an afterthought, a quick line of text before hitting enter. The gap between casual prompting and deliberate prompt engineering is measurable, repeatable, and closing faster than most organizations realize.
When your job title changes before your job description does
AI is reshaping professional roles faster than most organizations can update their org charts. Here is what that gap means for anyone who wants to stay relevant and well-compensated through the shift.
Why most RAG deployments fail before they go live
Retrieval-augmented generation promised to make enterprise AI actually useful on proprietary data. The gap between that promise and production reality reveals a set of specific, fixable problems that most teams keep hitting in the same order.
Choosing between ChatGPT, Claude and Gemini: what actually matters for professional use
ChatGPT, Claude and Gemini have each matured into capable platforms, but they make meaningfully different tradeoffs. Understanding those tradeoffs, rather than defaulting to the most familiar name, is what separates occasional AI users from people who consistently get better outputs.
What LLMs actually are, and why the technical details matter for business users
Most professionals using AI tools in 2026 are working with systems they only partially understand, and that gap has real costs. Knowing what large language models actually do, and where they break down, changes how you use them and how much you trust their output.
AI liability is no longer theoretical: what responsible deployment actually requires in 2026
Regulatory pressure, high-profile failures, and boardroom scrutiny have made responsible AI a concrete operational discipline, not a values statement. Here is what professional AI users need to understand about governance, accountability, and the practical steps that reduce real exposure.
AI agents in the enterprise: what breaks before it works
AI agents are moving from demo to deployment across industries, and the gap between the two is where most organizations stumble. Understanding what actually fails, and why, is more useful than another architecture diagram.
The prompt is the product: why your wording is now a business decision
Most professionals treat prompts as throwaway inputs, typed quickly and forgotten. The quality of what you write to an AI system is increasingly the difference between work that gets done well and work that gets redone.
How AI is reshaping hiring: what professionals need to know in 2026
AI screening tools now filter candidates before any human reads a resume, and generative AI is changing how people present themselves professionally. Understanding both sides of that equation is no longer optional for anyone managing a career or a team.
Why your RAG system keeps failing in production
Most enterprise RAG deployments look impressive in demos and disappoint in practice. The gap between a working prototype and a reliable production system is where the real engineering and strategic decisions happen.
ChatGPT, Claude, and Gemini: choosing the right tool instead of the default one
Most professionals default to one AI assistant and use it for everything, which is roughly equivalent to using a hammer for every job in the workshop. Understanding what each of the three major platforms actually does well, and where each one reliably falls short, is now a practical skill with measurable consequences for output quality.
Why LLMs still confabulate, and what you should actually do about it
Large language models can produce confident, well-formatted, completely wrong answers, and the problem is structural, not a bug waiting for a patch. Understanding why confabulation happens changes how you design workflows, evaluate outputs, and decide when not to use an LLM at all.
AI liability is no longer theoretical: what governance gaps cost companies now
Regulators across three continents are moving from framework-writing to enforcement, and the companies caught unprepared are paying for it in fines, reputational damage, and lost contracts. Here is what responsible AI governance actually looks like when the pressure is real.
AI agents in the enterprise: what actually breaks and how to fix it
AI agents are moving from demo to deployment across major organizations, and the gap between promised efficiency and real-world performance is proving instructive. Understanding where agent workflows fail is now more operationally valuable than understanding how they work in theory.
Prompt engineering in 2026: why most professionals are still leaving performance on the table
Most professionals now use LLMs daily, yet the gap between average and expert prompting is widening, not closing. This article breaks down what separates functional prompts from high-performance ones, and what that means for your day-to-day work.
How to think about AI fluency as a career asset in 2026
AI fluency has quietly become one of the clearest differentiators in hiring, promotion, and project leadership across industries. This article explains what that actually means in practice, and what to do about it.
Why your RAG system keeps failing in production
Most enterprise RAG deployments look impressive in demos and underperform in real workflows. Understanding exactly where they break, and why, is what separates teams that get lasting value from those stuck in an endless pilot loop.
ChatGPT, Claude and Gemini: how to pick the right tool for actual work
Most professionals using AI assistants in 2026 are still defaulting to one tool out of habit rather than fit. Understanding what each of the three dominant platforms does distinctly well changes both the quality of your outputs and the time you spend getting there.
What LLMs actually are, and why the architecture still matters in 2026
Most professionals using AI tools in 2026 have no idea what is actually happening inside them. Understanding the core mechanics of large language models does not require a PhD, and it changes how you use these systems productively.
AI liability is no longer theoretical: what governance gaps actually cost
Regulators across the EU, US, and Asia are moving from frameworks to enforcement, and the cost of inadequate AI governance is becoming measurable. Understanding where accountability breaks down in practice is now a core operational concern, not a compliance formality.
AI agents at work: what actually breaks and how to fix it before it costs you
AI agents are moving from demos to production, and the gap between the two is where most organizations lose time and credibility. Understanding where these systems fail in practice is more valuable right now than understanding how they work in theory.
Prompt engineering is now a core professional skill, are you keeping up?
The gap between professionals who know how to talk to AI systems and those who don't is widening fast. Mastering prompt engineering is no longer optional, it's becoming the new business literacy.
AI as your career accelerator: how to stay irreplaceable in 2026
AI is no longer a background technology, it is actively reshaping which professionals get promoted, hired, and trusted with high-stakes decisions. Here is how to position yourself on the right side of that shift.
RAG in the enterprise: why most deployments fail before they start
Retrieval-Augmented Generation promises to make your company's knowledge instantly accessible to AI, but the majority of enterprise deployments quietly underperform. The problem is rarely the AI model itself; it's everything that happens before the query reaches it.
ChatGPT, Claude, and Gemini: how to choose the right AI tool for real work
Three platforms now dominate the enterprise AI landscape, but they are not interchangeable. Understanding what each does distinctively well is the difference between getting marginal productivity gains and genuinely transforming how you work.
Why most professionals are using LLMs wrong, and what to do about it
Large language models are no longer a curiosity, they are infrastructure. But understanding how they actually work is the difference between a power user and an expensive button-clicker.