Building AI-Powered Drupal Sites: A Practical Overview

The AI Module Ecosystem

Drupal's AI integration story starts with the AI module — a contrib project that provides a provider-agnostic abstraction layer for connecting Drupal to LLM APIs. Rather than building OpenAI or Anthropic dependencies directly into your custom modules, the AI module gives you a unified interface:

$ai_provider = \Drupal::service('ai.provider');
$response = $ai_provider->chat('openai', 'gpt-4o', [
    ['role' => 'user', 'content' => 'Summarise this article: ' . $node->body->value],
]);

Swap the provider from openai to anthropic or mistral and the rest of your code stays unchanged. This is the right abstraction for enterprise Drupal work, where provider lock-in is a real concern.

Content Generation Workflows

The most immediate practical use case is content assistance in the editorial interface. The AI Interpolator module (built on top of the AI module) lets editors trigger AI actions directly from content forms:

• Generate a summary from a long article body
• Suggest SEO meta descriptions based on the page content
• Auto-tag content based on a taxonomy vocabulary
• Translate field values using an LLM when a formal translation service isn't available

These workflows keep editors in the Drupal admin rather than switching between tools, and the outputs are always suggestions — editors retain full control over what gets published.

Smart Search with Vector Embeddings

Standard Drupal search (Apache Solr or Elasticsearch via Search API) does keyword matching. It's good for exact queries but fails on semantic search — "articles about scaling Drupal" won't find content that never uses the word "scaling".

Vector search changes this. The workflow:

1. On content save, generate an embedding for the node body using an embeddings API (OpenAI text-embedding-3-small is cost-effective)
2. Store the embedding in a vector database (pgvector works well if you're already on PostgreSQL; Pinecone or Weaviate for dedicated vector storage)
3. At search time, embed the query and find the nearest neighbours by cosine similarity

This is non-trivial to implement cleanly in Drupal, but the Search API Vector Search module is making it progressively easier.

AI-Assisted Code Review in Drupal Modules

Beyond the editorial layer, LLMs are changing how Drupal developers work. I've been using Claude and Copilot for:

• Reviewing hook implementations for edge cases I might have missed
• Generating test stubs for custom modules (PHPUnit + KernelTestBase)
• Explaining unfamiliar Drupal APIs — the hook and plugin system has a lot of surface area, and LLMs are generally well-trained on the drupal.org documentation

The LLM doesn't know your specific business logic, but it knows the Drupal API surface extremely well. Use it for the "how do I do X in Drupal" questions and write the business logic yourself.

What to Watch

The Drupal AI initiative is moving fast. The roadmap includes deeper integration with the CKEditor 5 toolbar (AI-assisted writing inline), automatic alt-text generation for the Media Library, and an AI-powered site building assistant that can scaffold content types from a plain-English description. The foundation is solid — the next 18 months will be interesting.