AI-Powered Technical Documentation: How Scaleway uses Artificial Intelligence to transform user experience

AI-Powered Technical Documentation: How Scaleway uses Artificial Intelligence to transform user experience Deploy • Océane Franc • 15/10/24 • 6 min read

AI in Technical Documentation

Chatbots are increasingly used across various sectors to facilitate user interaction and support. But despite their widespread use, many chatbots fail to meet user expectations due to their inability to provide specific and contextually appropriate answers. When it comes to solving technical issues, we want to turn to humans for help rather than robots. Robots too often respond to a question with a generic message that doesn't even address the issue in the first place.

However, it’s a common misconception that all chatbots are created equal.

What if we could build a chatbot that is smart enough to comprehend a complex knowledge base and consolidate different content into a single, personalized answer for the user? Well, this isn't just a hypothetical, it's a reality.

Meet Devana: Scaleway's AI-driven Documentation Chatbot

We are introducing Devana, our AI-powered solution integrated into our Documentation website . Unlike traditional chatbots that rely on scripted and limited response mechanisms, Devana uses Machine Learning and Retrieval-Augmented Generation (RAG) systems to understand and interpret user questions effectively. An example is detailed in the chatbot’s architecture section.

Thanks to RAG, Devana retrieves relevant data from the Documentation website and generates accurate and contextually rich responses.

While our Algolia-powered search engine already enhances your search experience, Devana takes it further. It goes beyond keyword dependency by understanding the context of your queries and delivering accurate, personalized responses in natural language.

Key Concepts to Understand Devana’s AI-Powered Documentation Platform

In the context of Devana's platform, understanding the following key terms is essential:

Agent : An agent refers to an AI-powered entity designed to perform specific tasks autonomously. These agents can process natural language queries, execute commands, interact with users, and potentially even engage in more complex decision-making processes based on their training.

Identity : Identity is generally tied to user accounts or profiles linked to specific agents. This identity system manages permissions, tracks interactions, and ensures that the AI agents can securely and accurately associate actions and data with the correct user. Therefore, for Scaleway, we had to build our own Identity.

Prompt : Refers to the initial input given to a language model to generate a response or perform a task. In Retrieval-Augmented Generation, a prompt typically includes the initial query or text and additional information retrieved from external sources (in our case, the Documentation website).

GenAI Model : A GenAI model on Devana refers to the generative AI models, such as those based on large language models (LLMs), used to power the agents. In this context, Devana is positioned as a company specializing in data preparation to create complex, high-dimensional vector systems.

Embeddings : Embeddings refer to numerical representations of words, phrases, or other types of data that capture their semantic meaning. AI models create these embeddings to map complex and unstructured data (like text) into a continuous vector space, where similar items are placed closer together. Words with similar meanings will have similar embeddings, allowing the agent to process and generate more relevant responses. Devana takes this a step further by incorporating an additional layer of technology that vectorizes metadata (title, description, paragraph, tags…, etc.) alongside the text. This approach preserves the structure of documents, including elements like headings and subheadings, ensuring the AI comprehends not only the content but also its organizational context.

💡If you’re not familiar with the term Vector Space, think of it like a map where each word in a language is represented by a point. This map is not a flat surface but a space with many dimensions (like a 3D space, but with many more directions). Each word is like a point on this map. If two words are similar in meaning (like "cat" and "dog"), their points are close to each other. If two words are very different (like "cat" and "table"), their points are far apart. Words are placed based on their meanings. Words with similar meanings are close together, which helps AI systems understand and work with language more effectively.

Devana's Unique Architecture

Devana uses Scaleway’s H100 PCIe GPU and L40S GPU Instances to run its applications. From Devana’s interface, admin users can efficiently configure GenAI models, integrate new files into the knowledge base, and seamlessly personalize the prompts. The GPU Instances provide the computational power needed for these tasks, ensuring a smooth and responsive user experience. The diagram below illustrates the major components and the flow of information through the system we put in place.

Here's how the process works:

You submit a question to the chatbot from the Scaleway documentation website. For example, “Is Scaleway documentation open-source?”

The chatbot forwards the question to the Retrieval Model. RAG swings into action! It scans your chosen data source for entries that match your question.

With the most relevant information retrieved, RAG hands it over to the LLM, including your question and the appropriate details from the retrieved documents.

The LLM uses the provided information to generate an accurate and helpful response telling you that Scaleway Documentation is open-source with a link to our GitHub repository. 🥰

How Scaleway’s Documentation Powers Devana’s AI-Driven Responses

Scaleway's technical documentation is the backbone of our chatbot's knowledge base. It's the primary resource the AI consults to provide accurate answers and explanations to user queries. Devana offers several identity modes for responding to users: Limited, Creative, and Unlimited .

For our purposes, we decided to go with the Limited identity to ensure that the AI draws content solely from the Documentation website. This ensures that the chatbot's responses are strictly aligned with the approved and accurate information, preventing it from introducing perspectives or ideas that might be incorrect or stray from Scaleway's…