For many years, the model was relatively simple:

Query ⇨ Search results page ⇨ Click ⇨ Website

Today, this journey can no longer be taken for granted.

Google, Bing, and emerging AI-powered search platforms are increasingly able to understand complex questions, perform multiple searches, compare sources, and generate answers directly for users.

Google has introduced and progressively expanded AI Overviews and AI Mode. Microsoft is integrating search with Bing and Copilot, while systems such as ChatGPT Search are further transforming the concept of a search engine by combining web search with AI-generated answers.

The result is a significant shift in user behaviour: for many informational queries, users no longer need to visit multiple websites to build an answer themselves.

Search engines are therefore evolving from lists of documents into systems for retrieving, understanding, and synthesising information.

This does not mean that SEO is dead.

It means that its purpose is expanding.

We should no longer ask only:

“How can I rank this page?”

but also:

“How can I make this page a reliable source that a search engine or AI system can find, understand, retrieve, and cite?”

From SEO to AEO and GEO

In recent years, terms such as SEO, AEO, and GEO have become increasingly common.

SEO: Search Engine Optimization

Optimising websites and content to gain visibility in traditional search engines.

AEO: Answer Engine Optimization

Optimising content so that it can directly contribute to the answer provided to the user.

GEO: Generative Engine Optimization

Optimising a website’s presence within AI-powered and generative search experiences.

However, these should not be considered three completely separate disciplines.

SEO remains the foundation.

Google has clarified that AI Overviews and AI Mode continue to rely on its core Search ranking and quality systems and that no special technical requirements are needed to appear in generative search features.

Microsoft is also increasingly using the term GEO to describe how content participates in AI experiences and is selected as a source for generated answers.

We can therefore represent this evolution as:

SEO ⇨ Discovery

AEO ⇨ Answer

GEO ⇨ Citation & Generative Visibility

SEO enables content to be discovered.

AEO and GEO extend this concept towards making content easier to understand, retrieve, and use within generative answers.

Types of Queries and the Risk of Traffic Loss

The impact of generative search is not the same for every type of query.

Informational Queries: HIGH RISK

These are queries where the user is primarily looking for an explanation.

For example:

• What is the OAuth2 protocol?
• What does canonical URL mean?
• How does a CDN work?
• What is the difference between SEO and SEM?

For these queries, a generative answer can often provide a sufficiently complete explanation immediately.

The consequence is an increase in zero-click searches: searches that end without the user visiting an external website.

Comparative and Research Queries: MEDIUM/HIGH RISK

AI platforms are particularly effective at comparisons.

A question such as:

“Which CMS is better suited to an international healthcare portal: Drupal or WordPress?”

may require the system to compare:

• security;
• scalability;
• multi-country management;
• editorial workflows;
• SSO;
• privacy;
• integrations;
• costs;
• maintenance.

Google AI Mode and other generative systems can perform multiple searches to build this comparison directly.

Clicks remain important when users want to verify a source, explore a technical aspect in greater depth, read about real-world experience, or analyse more detailed data.

Transactional Queries: LOWER RISK

When users need to complete an action, websites continue to play a fundamental role.

For example:

• purchasing a product;
• booking a hotel;
• requesting a quote;
• signing up for a service;
• downloading software;
• contacting a company.

However, this area is evolving as well.

AI Agents are progressively transforming artificial intelligence from a system that simply provides answers into a technology capable of interacting with services and applications.

The evolution could therefore be represented as:

Search ⇨ Answer ⇨ Conversation ⇨ Action

How Generative Search Works

Not every search engine uses the same architecture, and we do not know every detail of how their systems work.

However, we can identify a general model.

1. Understanding Intent

The system interprets the request and attempts to understand:

• meaning;
• context;
• entities;
• intent;
• relationships between concepts.

As a result, an exact match between the keyword entered by the user and the text on a page is becoming less important.

2. Query Fan-Out

One of the most interesting features of generative search is query fan-out.

Google has officially confirmed that AI Overviews and AI Mode can perform multiple related searches across different subtopics and data sources.

A complex query can therefore be broken down into several secondary searches.

For example:

“What is the best CMS platform for a European HCP portal?”

could generate searches related to:

healthcare CMS

Drupal security

WordPress healthcare

GDPR CMS

multi-country CMS

HCP authentication

SSO healthcare portal

Content therefore does not necessarily need to match the exact wording of the user’s original query to become a relevant source.

Retrieval, Grounding, and RAG

One of the fundamental concepts behind AI-powered search is grounding.

A Large Language Model contains knowledge acquired during training, but when answering questions that require current information, it can use external sources.

One approach used for this is Retrieval-Augmented Generation (RAG).

In simplified terms:

Query
⇩
Retrieval
⇩
Source Selection
⇩
Information Extraction
⇩
Answer Generation
⇩
Citations / Links

Google explicitly confirms the use of RAG and grounding techniques within its generative search features.

Microsoft also considers grounding a central component of the AI-powered web: web sources allow generative systems to connect their answers to up-to-date and verifiable information.

For SEO and content professionals, this means something important:

before content can be used in an AI-generated answer, it needs to be discoverable, understandable, and considered sufficiently relevant.

This is precisely where traditional SEO continues to play a fundamental role.

Google, Bing, and the New Search Landscape

This transformation is not limited to Google.

Google

Google primarily operates through:

• traditional Search;
• AI Overviews;
• AI Mode.

AI Overviews synthesise information directly within the SERP when Google determines that doing so adds value to the search experience.

AI Mode extends this model further by enabling more complex searches, comparisons, and conversational follow-up questions.

Both can use query fan-out to discover sources that may differ from those surfaced by a single traditional search query.

Bing and Microsoft Copilot

Microsoft is following a similar direction through Bing and Copilot.

One particularly interesting development is the introduction of AI Performance in Bing Webmaster Tools.

The report allows publishers to analyse metrics such as:

• total citations;
• cited pages;
• grounding queries;
• citation trends over time.

Microsoft is therefore beginning to distinguish more explicitly between traditional SEO visibility and visibility within AI-generated answers.

This may provide an early indication of how SEO analytics tools will evolve over the coming years.

ChatGPT Search and Emerging AI Search Engines

Alongside traditional search engines, we are seeing the growth of systems where search is integrated directly into an AI assistant.

ChatGPT Search, for example, can search the web and use external pages as sources for its answers.

For publishers, this introduces another dimension of organic visibility.

There is no longer only:

Google ranking

but rather an ecosystem consisting of:

Search visibility

AI visibility

Citation visibility

Brand visibility

A website can therefore generate value even when its content is used as a source without necessarily generating an immediate click.

From Keywords to Topics, Intent, Entities, and Context

Keywords are not disappearing.

Their strategic role, however, is changing.

The traditional approach:

One keyword ⇨ One page

is becoming less effective when applied too rigidly.

A modern approach should instead consider:

Keyword ⇨ Topic ⇨ Intent ⇨ Entity ⇨ Context

Google has also clarified that there is no need to create dozens of pages simply to cover every possible long-tail variation.

Modern search systems can understand synonyms, concepts, and entities even when there is no literal match with the user’s query.

Building comprehensive semantic coverage of a topic is therefore becoming increasingly important.

How to Write Content for AI-Powered Search

There is no magic formula for getting cited.

Nor is there an ideal paragraph length that guarantees inclusion in an AI-generated answer.

The objective should be to make content easy to understand for both people and information retrieval systems.

A good structure can include:

• descriptive headings;
• clear paragraphs;
• direct answers when appropriate;
• tables for comparing data;
• lists when they improve readability;
• precise definitions;
• examples;
• verifiable sources;
• connections between related concepts.

The old idea of an Atomic Answer that must always be 40-60 words long should therefore not be considered an SEO rule.

The answer simply needs to be clear, complete, and proportionate to the question.

The Real Competitive Advantage: Non-Commodity Content

Artificial intelligence makes it extremely easy to produce generic content.

And that is precisely why generic content is losing value.

Google uses the concept of non-commodity content to describe content that adds something beyond information that is already widely available.

This may include:

• first-hand experience;
• tests;
• benchmarks;
• proprietary data;
• original research;
• case studies;
• screenshots;
• real-world examples;
• well-supported expert opinions;
• internally developed methodologies.

An article that simply repeats:

“10 benefits of cloud computing”

can easily be summarised by an AI system.

An article presenting:

“How we reduced response times by 38% by migrating this architecture to Azure”

contains information that the model is unlikely to find elsewhere.

This distinction will become increasingly important.

E-E-A-T in AI-Powered Search

Experience, Expertise, Authoritativeness, and Trustworthiness continue to represent important concepts for assessing content quality.

However, they should not be interpreted as a single “AI ranking factor”.

The fundamental principle remains demonstrating that the content is supported by genuine experience, expertise, and verifiable information.

Experience

Demonstrate real-world experience through:

• tests;
• real cases;
• screenshots;
• original photos;
• problems encountered;
• results achieved.

Expertise

Demonstrate expertise through:

• technical insights;
• appropriate terminology;
• sources;
• data;
• accurate explanations;
• clear author profiles.

Authoritativeness

Build authority through:

• citations;
• backlinks;
• mentions;
• publications;
• industry reputation.

Trustworthiness

Strengthen trust through:

• verifiable sources;
• identifiable authors;
• editorial transparency;
• publication and update dates;
• clear company information;
• website security.

Structured Data: Important, but Without the Myths

Schema.org and structured data remain important SEO tools.

They help search engines understand certain types of information and may enable specific rich results.

However, they should not be considered a requirement for being used in AI-generated answers.

Google has clarified that there is no special Schema.org markup for AI Overviews or AI Mode.

Structured data should therefore be implemented when it accurately represents the content on the page and provides genuine value to Search.

For example:

• Article;
• Product;
• Organization;
• Person;
• Event;
• BreadcrumbList;
• LocalBusiness.

The principle remains simple:

Use structured markup when it adds meaning; do not create artificial markup simply to “convince the AI”.

Images, Video, and Multimodal Content

Search is becoming increasingly multimodal.

Answers can include:

• text;
• images;
• video;
• products;
• maps;
• structured information;
• interactive elements.

Google explicitly recommends complementing textual content with high-quality images and videos where appropriate.

It is therefore important to consider:

• image quality;
• alt text;
• file names;
• surrounding text;
• captions;
• video SEO;
• image/video sitemaps where appropriate;
• consistency between visual and textual content.

An original diagram or technical screenshot can provide significantly greater informational value than a generic stock image.

Topic Clusters and Internal Linking

Generative search further reinforces the importance of topical authority.

A single isolated article is unlikely to demonstrate the same depth as a connected content ecosystem.

An effective structure might look like this:

Pillar Page

Deep Dive A

Deep Dive B

Case Study

FAQ

Tutorial

Comparison

Internal linking helps search engines understand:

• relationships between content;
• hierarchy;
• context;
• topical depth.

It also improves crawling and content discovery.

Technical SEO: What Does Not Change

Artificial intelligence does not eliminate the need for a technically solid website.

The fundamentals remain essential:

• crawlability;
• indexability;
• robots.txt;
• XML sitemaps;
• canonical tags;
• correct redirects;
• HTTP status codes;
• performance;
• mobile usability;
• accessibility;
• JavaScript rendering;
• internal linking;
• understandable HTML;
• important content available in textual form.

If a search engine cannot properly retrieve content, it is unlikely to be able to use that content as a source.

The New SEO KPIs

One of the biggest mistakes will be continuing to measure success exclusively through organic traffic.

Traditional KPIs remain important:

• rankings;
• impressions;
• CTR;
• clicks;
• organic sessions;
• conversions.

However, they increasingly need to be complemented by new metrics:

• citations in AI-generated answers;
• cited pages;
• branded searches;
• brand mentions;
• direct traffic;
• referrals from AI platforms;
• engagement;
• conversion rate;
• visibility for strategically relevant queries.

Bing has already started this transition through AI Performance in Bing Webmaster Tools, providing data on citations and grounding queries from Copilot, Bing AI, and selected partner experiences.

Google, on the other hand, currently includes AI Overviews and AI Mode within the overall Web search traffic reported in Search Console.

Measuring AI visibility is therefore still a rapidly evolving area.

SEO Is Not Disappearing: Its Objective Is Changing

For years, the primary SEO objective was:

Achieve a ranking
⇩
Generate a click
⇩
Bring the user to the website

Today, the journey can increasingly become:

Be discovered
⇩
Be considered relevant
⇩
Be retrieved as a source
⇩
Be cited
⇩
Build trust
⇩
Generate a visit or an action

This fundamentally changes how we should assess the value of content.

Content can contribute to brand visibility even before a click occurs.

Conclusion

SEO is not dead.

On the contrary, it remains part of the infrastructure on which much of the new AI-powered search ecosystem is being built.

What is changing is the context.

We are no longer optimising solely for a results page made up of ten blue links.

We are optimising for an ecosystem where several different experiences coexist:

Search Engine

Answer Engine

Generative Engine

AI Assistant

AI Agent

Google AI Overviews, AI Mode, Bing, Microsoft Copilot, and emerging AI platforms are progressively transforming how information is discovered and used.

The future of SEO is therefore not about finding tricks to “please LLMs”.

It is about building technically accessible websites and creating content that has something genuinely valuable to offer:

experience, data, expertise, clarity, authority, and original information.

In the past, the main objective was to get the click.

Today, the objective is also to become a source for the answer.

Tomorrow, it may be about becoming both a source and a service that AI agents can interact with directly.

Primary Sources:
Google Search Central – AI features and your website,Google – Generative AI optimization guide,Bing Webmaster – AI Performance

L'articolo How SEO Is Changing with the Advent of AI Overview, AI Mode, and Generative Search proviene da Giovanni Anfuso.

Joomla is one of the historic open-source CMS platforms, often positioned between the simplicity of WordPress and the enterprise-level complexity of Drupal. Over the years, it has aimed to balance flexibility, structural control, and ease of use, catering to both developers and advanced users.
In this article, we analyze Joomla as a platform: its strengths, areas for improvement, technical evolution, and its role in today’s landscape dominated by APIs, headless architectures, and Artificial Intelligence.

Strengths of Joomla

Joomla offers several features that make it a valid solution in specific contexts:

• Native MVC structure
  Joomla has long adopted the Model-View-Controller pattern, providing a clear separation between logic, data, and presentation, appreciated by developers.
• Advanced user management and ACL
  Its permission system is among the most comprehensive in traditional CMSs, allowing granular configurations without external extensions.
• Built-in multilingual support
  Unlike other CMSs, Joomla offers native multilingual support, simplifying the management of international websites.
• Extensibility via components, modules, and plugins
  The ecosystem is well-structured, enabling additional functionality while maintaining good code organization.
• Historic and active open-source community
  Joomla boasts a strong community that has helped keep it alive and up-to-date over time.

Areas for Improvement

Despite its strengths, Joomla has shown challenges in maintaining a competitive position in the modern landscape:

• Less dynamic ecosystem
  Compared to WordPress and Drupal, the number of actively maintained extensions is smaller.
• Hybrid learning curve
  Not immediately beginner-friendly, yet not fully “developer-first” for those seeking complete control like Drupal.
• Admin UX and frontend
  The interface has improved over time but remains less intuitive compared to more modern solutions.
• Slow adoption of emerging trends
  Headless CMS, JAMstack, and microservices are supported, but they are not yet central to Joomla’s ecosystem.

Joomla’s Evolution Over Time

Joomla has gone through several evolutionary phases:

• Joomla 2.x – 3.x
  Consolidation of the MVC structure, improved user management, and extensions.
• Joomla 4
  Major code refactoring, greater adherence to modern PHP standards, improvements in security, accessibility, and performance.
• Joomla 5
  Focus on stability, compatibility with recent PHP versions, and enhanced developer experience.

This evolution shows a clear attempt at modernization, although more gradual compared to other CMS platforms.

Joomla in the Current Context: APIs, Headless, and AI

Modern web development demands increasingly flexible and integrable platforms:

• APIs and Headless
  Joomla supports REST APIs and can be used in headless architectures, but it was not initially designed with this philosophy in mind like Drupal or pure headless CMSs.
• Automation and AI
  Integration with AI tools mainly occurs via third-party extensions:
  • Content generation and optimization
  • Automatic tagging
  • Editorial and SEO assistance
• Intelligent personalization
  While the potential exists, AI adoption is not yet a core part of the official roadmap.
  In an AI-first context, Joomla will need deeper integrations and a more innovative extension ecosystem to remain competitive.

Conclusion

Joomla remains a solid CMS, suitable for medium-complexity projects requiring a strong structure, advanced user management, and content control. However, it faces competition from platforms more aggressive in innovation, such as Drupal in enterprise environments and WordPress in terms of reach and ecosystem.

The future of Joomla will depend on its ability to:

• Enhance the developer experience
• Integrate APIs, headless architectures, and AI natively
• Refresh and modernize its extension ecosystem

In my view, if Joomla succeeds in this path, it could carve out a more defined role in the landscape of modern CMS platforms.

L'articolo Joomla Ecosystem: Analyzing the CMS Between Flexibility, Limitations, and Evolution proviene da Giovanni Anfuso.

Drupal is one of the longest-standing and most versatile CMS platforms on the market, known for its flexibility and modularity. Over the years, it has evolved from a platform for complex websites to a tool capable of supporting enterprise projects, community portals, and advanced digital platforms. This article examines Drupal’s strengths and challenges, its evolution over time, and future prospects, particularly with the integration of Artificial Intelligence technologies.

Strengths of Drupal

Drupal stands out for several key features that make it a robust CMS appreciated by developers:

Modularity and flexibility: Thanks to its modular architecture, functionalities can be extended without compromising the core.
Advanced security: Regular updates and an active community ensure protection even for high-risk sites.
Complex content management: Supports custom content types, taxonomies, and advanced views.
Scalability: Ideal for enterprise websites, government portals, and high-traffic platforms.
Community and ecosystem: Thousands of modules and themes, alongside a global developer community.

Areas for Improvement

Despite its strengths, Drupal presents some challenges for developers and organizations:

Steep learning curve: The CMS complexity can be daunting for those coming from WordPress or more “out-of-the-box” solutions.
Frontend management not straightforward: Even with Twig, design and frontend integration require advanced skills.
Performance on small sites: Without caching and optimization, simple sites can perform slower than lighter solutions.
Module updates and compatibility: Some third-party modules require manual updates, occasionally causing conflicts.

Historical Evolution of Drupal

Drupal has undergone significant evolutionary steps:

Versions 6–7: Consolidation of content management and first developer APIs.
Version 8: Introduction of Symfony, REST API, greater adherence to modern standards.
Versions 9, 10, and 11: Improvements in security, modern PHP compatibility, integration with headless tools, and support for advanced editors like CKEditor.

This evolution has allowed Drupal to adapt to enterprise requirements and the growing demand for headless and omnichannel architectures.

Drupal and the Rise of AI

Artificial Intelligence is transforming the web, and Drupal is embracing these trends:

Content intelligence: Modules for automatic content suggestions, smart tagging, and text generation.
Advanced personalization: AI to suggest relevant content to different users in real-time.
Workflow automation: From SEO optimization to media management, AI can reduce editors’ workload.
Integration with external tools: APIs for NLP, data analysis, and predictive systems.

Adopting AI in Drupal represents an opportunity to simplify development, enhance UX, and deliver personalized content, while maintaining the platform’s security and flexibility.

Conclusion

Drupal remains a reference platform for developers and organizations seeking a powerful, scalable, and secure CMS. Its strengths, including modularity, security, and advanced content management, keep it competitive, while challenges such as the learning curve and performance can be mitigated through best practices and modern tools.

With AI integration, Drupal is poised to become not just a CMS for complex content, but an intelligent platform capable of adapting dynamically and predictively to the needs of users and development teams.

L'articolo Drupal Ecosystem: Strengths, Challenges, and Prospects with the Rise of AI proviene da Giovanni Anfuso.

Technical Documentation and Design Architecture: Why They Matter

Technical documentation is not an accessory to a project-it is one of its foundational pillars. Specifically, the design architecture of software, an app, or a web project represents:

• The overall system vision
• Long-term structural decisions
• Technical and organizational constraints
• Rules for project evolution

A well-documented architecture allows a project to:

• Scale over time
• Onboard new developers efficiently
• Reduce ambiguity and misunderstandings
• Support informed decision-making

Software Architect vs. Senior Developer

A software architect is not simply a highly experienced developer. The key difference lies in the primary medium they work with.

Senior Developer:

• Implements solutions through code
• Optimizes components and features
• Solves complex technical problems

Software Architect:

• Implements ideas through documentation
• Translates abstract visions into shared systems
• Coordinates people, processes, and technology
• Builds consensus before writing code

In short, code is a consequence of architectural decisions, not their starting point.

The Organizational Role of the Project Architect

An effective software architect acts as a facilitator of decision-making. Their work involves producing clear, targeted, and purpose-driven documents, including:

• Architecture overviews
• Development plans
• Project proposals
• Developer estimates and forecasts
• Technology menus and architectural choices
• Problem statements
• Post-mortem analyses

These documents do more than “explain”; they align teams, guide decisions, and assign responsibility.

How to Write Effective Technical Documentation

Good technical documentation follows precise rules, focusing on maximum information density and clarity.

Key Principles:

• Bullet points: Increase information density and reduce ambiguity
• Clear headings: Facilitate organization, consultation, and archiving
• Chronological storage: Documents are historical artifacts, not updated for “current relevance”
• Defined purpose: Each document must serve a specific, measurable goal

Documents as artifacts, not living resources:
A common mistake is treating documentation as something that must be continuously updated. Technical documents should be:

• Timely
• Contextualized
• Linked to a specific decision or project phase

This approach allows teams to reconstruct the why behind choices, not just the what.

The Real Bottleneck in Software Development

A key, often counterintuitive insight:

The greatest bottlenecks in software development are not technical-they are communicative and decision-making related.

Common problems include:

• Undocumented decisions
• Implicit assumptions
• Lack of consensus
• Ambiguities in requirements
• Divergent visions among teams

Well-designed technical documentation drastically reduces these risks.

Conclusion

Design architecture and technical documentation are strategic tools, not bureaucratic overhead.
The software architect is the professional who enables ideas to become real, shared, and sustainable systems-through writing first, and code second.

In an increasingly complex digital ecosystem, investing in documentation is investing in better decision-making and long-term project success.

L'articolo Technical Documentation and Software Architecture: The True Value of the Project Architect proviene da Giovanni Anfuso.

WordPress and Its Ecosystem

WordPress is the most widely used open-source CMS in the world, a software platform for creating websites, blogs, and dynamic applications based on PHP and MySQL. Thanks to its flexibility, extensive library of plugins and themes, and global community, it is the choice of millions of developers and companies.
Learn more at: Wikipedia

The WordPress ecosystem is not just the core; it includes a range of components, tools, and communities that collaborate to expand its functionalities and development opportunities.

WordPress Architecture

WordPress is built on a modular architecture that can be extended via standard components:

• Core: the heart of the CMS, providing basic functions for managing content, users, pages, posts, and media.
• Themes: define the website’s visual appearance and can be customized or built from scratch.
• Plugins: extend functionality (e-commerce, SEO, performance, forums, etc.).
• Database: MySQL or MariaDB stores content, settings, and configurations.
• REST API: allows interaction between WordPress and external applications.
• Block Editor (Gutenberg): visual editing system with a block-based structure.

Learn more at: Wikipedia

This architecture allows building simple or complex sites but requires a solid understanding of the abstraction layers and how they interact.

Official Documentation: Standards, Guidelines, and Orientation

The official WordPress documentation (Codex, Developer Resources, and Learn.WordPress) primarily serves as a standards reference: it provides accepted and recommended guidelines on how to perform tasks and how internal tools function. It does not merely give “don’t do it because it might go wrong” advice but defines how to do a task correctly and which standards are recognized.

Examples of official documentation:

• Learn.WordPress.org – Structured learning pathways for users and developers.
• Developer Handbook (Developer Resources) – Guidelines on APIs, plugins, and themes.
• WordPress Codex – Historical user manual and developer reference.

Important: official documentation is not necessarily a complete step-by-step learning path but rather an accurate description of how standards and APIs work.

Recommended Learning Order for WordPress

1. Understanding WordPress as a CMS
   Start with the basics:
   • What a CMS is and how WordPress works.
   • Differences between WordPress.com and WordPress.org.
   • Dashboard, posts, pages, media, and user roles.
     This phase is crucial to contextualize everything else.
2. Installation and Configuration
   • Local environments (Local, XAMPP, Docker).
   • General settings, permalinks, media.
   • Basic security and updates.
3. Themes and Customization
   • Child and custom themes.
   • Modifying template files.
   • Block editor (Gutenberg) and Full Site Editing.
4. Plugins and Advanced Features
   • Key plugins: SEO, performance, caching.
   • How to install and configure.
   • When custom development is required.
5. Advanced Development (for Developers)
   • Core languages: HTML, CSS, JavaScript, PHP.
   • WordPress hooks: actions and filters.
   • Creating custom plugins and themes.
   • REST API and performance.

The Roadmap from roadmap.sh

The roadmap.sh page provides a structured guide to WordPress development in 2026. While useful as a general orientation, it is important to note that it is not official WordPress documentation but an educational roadmap created by the community with support from Melapress.

Thanks to the author for the synthesis, but it is important to note:

• It is not always supported by quality metrics or established benchmarks — it is more an ideal path than a rigid rule.
• It often lacks direct references to real-world use cases: the roadmap does not include tools for automated quality control or explicit guidance on avoiding bad practices.
• It does not consider resource budgeting, i.e., realistic allocation of time and resources for each learning or production level.
• Practices like overusing page builders without understanding standards or failing to test for performance/security can lead to large-scale issues.

In summary: roadmap.sh is excellent as a high-level guide but should be complemented with official documentation and a critical, best-practice-based approach.

Common Issues and “Bad Practices” to Avoid

Many learners (and professionals) stumble on recurring mistakes:

• Relying exclusively on page builders without understanding the core and standard themes.
• Ignoring security and daily maintenance (updates, backups).
• Not testing performance and SEO objectively.
• Not knowing WordPress coding standards, leading to non-performant plugins or themes.

These errors are not caused by the wrong tool but by a lack of disciplined learning roadmap based on real standards.

Conclusion

Successfully learning WordPress means understanding the ecosystem as a whole: core, themes, plugins, APIs, and community. Official documentation is essential as a standards and reference guide, while third-party resources like roadmap.sh are useful to understand the development path, provided they are integrated with real best practices, quality control, and performance evaluation.

L'articolo WordPress: what it is, how it works, and the order in which to learn it proviene da Giovanni Anfuso.