Blog /AI

Your marketing team just published an important policy update. Two hours later, a user asks your AI chatbot about the new policy, and it confidently explains the old version—completely unaware that anything changed. The user, confused by the discrepancy with what they read on the website, opens a support ticket asking why the chatbot is "broken." Your team explains that the chatbot needs manual re-indexing, which happens nightly. The user's trust in your AI system drops significantly.

You're starting a new RAG project and face a decision that will shape your next 6-12 months: use a framework like LangChain, or build directly with the OpenAI API? The internet offers conflicting advice. X’s threads call LangChain "overkill" and "too much abstraction." Blog posts praise its mature patterns and ecosystem. Your team splits between "let's move fast with the framework" and "we should control our own code."

It starts like this: a user types, “How do I reset my password?” and waits… 25 seconds… 30 seconds… before giving up and emailing support. Behind the scenes, your AI chatbot does have the right answer — but it’s too slow, too costly, and users are walking away. The latest API bill? $5,000, mostly for answering the same dozen questions again and again.

Your AI chatbot may respond quickly, but are its answers correct? Many organizations implementing RAG (Retrieval-Augmented Generation) chatbots discover a frustrating truth: semantic similarity doesn't equal relevance. For example, a user may ask about "implementing zero-trust security architecture in hybrid cloud environments," and the system confidently returns articles about "cloud security," but they discuss basic firewall rules instead of zero-trust principles.

Artificial intelligence is revolutionizing the way we manage content in CMS systems. Drupal is not lagging behind and offers more and more AI modules to automate editorial processes. In this article, I will present four of the latest AI-powered solutions for Drupal that can significantly improve content work and project management. This post is based on my video from the Modern Drupal channel. 

Artificial intelligence in content management systems enables the automation of publishing processes that previously required manual verification. By using the appropriate modules in Drupal, you can configure a system that automatically approves or rejects content based on predefined AI criteria. In this article, I will present a complete guide to configuring such a system, along with practical examples. Feel free to read the post or watch an episode of the Modern Drupal series.

Drupal, as a system that focuses on flexibility and a modern approach to content publishing, is rapidly adopting AI solutions. It offers tools for integrating with artificial intelligence models (including OpenAI), which allows automating many processes: from content generation to translation and language analysis. In this article, I’ll introduce the operation of AI modules for Drupal, demonstrating their use with examples from a practical demo. 

Our previous article on AI terms introduced you to the basic dictionary. In this part, we'll dive into more specialized phrases that will help you understand the technical discourse and enable you to discuss AI systems with more tech-oriented people. You'll also find some explanations of the inner workings of existing AI solutions such as ChatGPT, Stable Diffusion, HuggingFace, etc.

After the „AI revolution of 2022,” there is one simple conclusion – AI isn’t a fad that will go away anytime soon, but it will stay with us for some time. This spawned new positions within the industry and a lot of new phrases. There are many terms when discussing AI. Some of them are self-explanatory, and some can be confusing and unclear. Hopefully, after reading our articles on this topic, you won’t be surprised when someone throws this new terminology at you.