Practical UX, user research, information architecture, accessibility, CRO, and strategy guides based on methods used in real digital product work.
Showing 52 of 196 guides
Conversion Optimisation
Understanding the real routes users take through a product, including loops, backtracking, and unexpected paths.
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Measuring how users adopt, engage with, and return to specific features to understand product value.
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Using experienced judgement to identify friction, risks, and improvement opportunities across a product or journey.
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Assessing how easily new users can understand and start using a product for the first time.
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Evaluating how easily users move through a live product or prototype so journeys, menus, and pathways can be improved.
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Creating clear, scalable structures for content, products, and information so organisation and navigation make sense.
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Assessing whether orientation cues and contextual signals help users understand where they are and how to move through a product.
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Visualising the broader human experience around a goal so teams can understand context, unmet needs, and opportunities beyond the product.
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Understanding why people choose, switch, or abandon products by exploring the motivations and forces behind real decisions.
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Defining what users are really trying to achieve so products can be shaped around intent, priorities, and real needs.
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Visualising the wider system around a product or service so dependencies, relationships, risks, and opportunities become clearer.
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Delivering a service manually to validate user value and demand before investing in automation or full product build.
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Measuring real demand for unbuilt ideas by tracking interactions with realistic entry points before investing in development.
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Testing labels and product language to ensure terms are clear, recognisable, and consistently understood.
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Mapping how users think and reason about a system to align product structure with real expectations.
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Identifying the functional, emotional, and social jobs users need done to guide product and UX decisions.
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Categorising features by satisfaction impact to prioritise must-haves, performance drivers, and delighters.
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Mapping assumptions by certainty and impact to prioritise validation and reduce product and design risk.
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Testing high-risk assumptions with lightweight experiments to reduce uncertainty and guide product decisions with evidence.
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Analysing support requests to uncover recurring user pain points, prioritise fixes, and reduce product friction.
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Analysing customer reviews to uncover recurring pain points, sentiment trends, and opportunities for UX and product improvement.
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Examining complaints to identify recurring root causes, reduce frustration, and prioritise UX and product improvements.
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Using repeated ‘why’ questioning to identify root causes behind recurring UX and product issues.
Read guideArtificial Intelligence
What retrieval-augmented generation is, how it improves AI accuracy, and what designers and product teams need to know when working with it.
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What human-in-the-loop design is, how it reduces risk in AI systems, and what product and UX teams need to consider when deciding where humans should be involved.
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What LLMs are, how they generate responses, and what designers and product people need to understand to work effectively with AI-powered features.
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What AI hallucinations are, why they happen, how to spot them, and how to design AI products that account for them.
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What fine-tuning does to an AI model, when it is worth doing, and what product and design teams need to know before commissioning it.
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What system prompts do, how they define an AI's role and constraints, and what product and design teams need to know when working with them.
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What a context window is, how it affects AI behaviour across a conversation, and what product and design teams need to account for when building AI features.
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What tokens are, how tokenisation affects AI behaviour and cost, and what designers and product teams need to know when building AI features.
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What embeddings do, how they allow AI to understand meaning rather than just match words, and what product teams need to know when working with semantic search or RAG.
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What AI agents are, how they work, and what product and design teams need to understand when building or evaluating agentic AI features.
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What prompt engineering involves, how it shapes AI output quality, and what product and design teams need to know to do it well.
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What AI bias is, where it comes from, how it affects real users, and what designers and product teams should do about it.
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What foundation models are, how they differ from traditional software, and what product and design teams need to know when building on top of them.
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What guardrails are, how they prevent AI from behaving in harmful or off-brand ways, and what product and design teams need to consider when defining and implementing them.
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What multimodal AI can process and generate beyond text, how it expands what is possible in product design, and what teams need to consider when working with it.
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What temperature controls in AI models, how it affects the range and consistency of responses, and when to adjust it for different product use cases.
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What training data is, how it shapes what an AI model knows and assumes, and what product and design teams need to understand about its role in AI product quality.
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What reinforcement learning from human feedback is, how it is used to make AI more helpful and appropriate, and what product and design teams need to understand about its role in model development.
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What vector databases do, how they enable semantic search and RAG, and what product and design teams need to know when working with AI systems that use them.
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What inference is, how it differs from training, and what product and design teams need to understand about its implications for speed, cost, and reliability.
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What synthetic data is, how it is generated, and what product and design teams need to know about its role in training, testing, and evaluation.
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What transparency in AI products involves, why users need to know when they are interacting with AI, and how to design for honest, clear communication about AI use.
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What prompt injection attacks are, how they work, and what product and design teams need to understand to protect AI features against them.
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What zero-shot and few-shot learning are, how they affect AI output quality, and how product and design teams can use them to improve AI performance without technical complexity.
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What model evaluation involves, how to assess whether an AI model actually works for your use case, and what product and design teams need to know to contribute to evaluation effectively.
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What latency means in AI systems, how it affects user experience, and what product and design teams can do to manage it effectively.
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What AI safety and alignment involve, why they are not just researcher concerns, and what product and design teams need to know to build responsibly with AI.
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What neural networks are, how they relate to modern AI, and what product and design teams need to know without needing to understand the mathematics.
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What chain-of-thought prompting does, when it helps, and how product and design teams can use it to get more reliable outputs from AI on complex tasks.
Read guideLET'S WORK TOGETHER
UX, research and product leadership for teams tackling complex digital services. The work usually starts where things have become harder than they need to be: unclear journeys, inconsistent products, competing priorities, or teams trying to move forward without a clear direction. I help simplify the problem, shape the right next step, and turn complexity into something people can actually use.
Previous feedback
I had a fantastic experience working with Andy. One of his most impressive achievements during our time at NHS HEE was masterminding a deeply complex information architecture for a new platform that brought together a large number of legacy websites.
Will Parkhouse
Senior Content Designer