DUST TRAINING: BUILD AI ASSISTANTS THAT ACTUALLY WORK

We start at the beginning: create your workspace, understand the interface, explore what Dust can do. You'll learn the core concepts: assistants vs apps, conversations vs workflows, data sources vs actions. You'll understand how Dust differs from ChatGPT and when to use it. We'll create your first simple assistant. Nothing fancy, just a basic conversational agent to understand the mechanics. You'll configure instructions, choose a model (GPT-4, Claude, etc.), and test different behaviors. You'll also learn workspace management: invite team members, set permissions, understand usage limits and pricing. At the end of this module, you know how Dust works and have created your first functional assistant. You understand the platform's potential and limitations.

Now we connect your assistants to real data. This is where Dust becomes powerful: assistants that know your company's actual information. You'll integrate Notion pages, Google Drive folders, Slack channels, and other data sources. You'll understand how Dust indexes content, manages updates, and retrieves relevant information during conversations. We'll configure data source permissions so assistants only access what they should. You'll learn to structure your knowledge base for optimal retrieval and avoid common pitfalls like outdated data or permission issues. You'll also master context management: how much data to feed your assistant, when to use semantic search vs full-text search, and how to handle large knowledge bases without hitting context limits. At the end of this module, your assistants have access to company knowledge. They can answer questions based on your actual documents, not generic training data.

Here we learn to make your assistants reliable and consistent. Not just smart, but predictable. You'll master prompt engineering: how to write instructions that give consistent results, structure multi-step reasoning, handle edge cases, and prevent hallucinations. We'll test different approaches and see what works. We'll configure assistant personality and tone. Whether you need a formal customer support agent or a casual internal helper, you'll learn to tune behavior through instructions and examples. You'll also learn model selection: when to use GPT-4 vs GPT-3.5 vs Claude, how to balance cost and performance, and which models work best for specific tasks like summarization, extraction, or generation. We'll handle common issues: assistants that don't follow instructions, responses that are too verbose or too short, and how to iterate on prompts until they work. At the end of this module, you can design assistants that behave exactly how you want them to. You understand why they fail and how to fix it.

Now your assistants start doing things. Not just answering questions, but triggering actual workflows. You'll learn Dust actions: pre-built integrations and custom API calls. You'll connect assistants to tools like Slack (send messages), Notion (create pages), databases (update records), and external APIs. We'll build an assistant that takes user input and performs actions: create a task, update a CRM, send a notification, or trigger a webhook. You'll chain multiple actions together for complex workflows. You'll handle API authentication, manage secrets securely, and deal with rate limits and errors. We'll test different scenarios and make sure your actions are robust. You'll also learn when to use actions vs when to keep it conversational. Not everything needs automation. At the end of this module, your assistants can interact with your entire tech stack. They're not just chatbots, they're autonomous agents that get work done.

Here we go beyond simple assistants and build complex AI workflows. You'll learn Dust apps: structured workflows that chain multiple steps, decisions, and actions. Unlike conversations, apps follow a predefined logic and can handle batch processing. We'll build workflows like: document analysis pipelines, automated content generation, data extraction and transformation, and approval workflows with human-in-the-loop. You'll master workflow design: when to use assistants vs apps, how to handle errors and retries, and how to monitor execution. We'll also cover advanced features like conditional logic, loops, and parallel processing. You'll learn to combine multiple assistants: have one that gathers information, another that analyzes it, and a third that acts on it. You'll understand orchestration patterns and when to keep it simple vs when to go complex. At the end of this module, you can build production-grade AI systems that scale beyond individual conversations.

Last module: we deploy everything and make sure it works in production. You'll learn team deployment: share assistants with your company, train users, gather feedback, and iterate. You'll set up proper governance: who can create assistants, who can access which data, and how to monitor usage. We'll cover monitoring and optimization: track performance, identify bottlenecks, reduce costs, and improve response quality over time. You'll use Dust analytics to understand how your assistants are used. We'll work on real cases from participants. Whether it's customer support automation, internal knowledge management, content generation, or process automation, we'll build it together and troubleshoot issues. You'll also learn maintenance: keep data sources up to date, update prompts as models evolve, handle breaking changes, and scale as usage grows. At the end of this module, you have production assistants running in your company. You know how to maintain them, improve them, and train your team to use them effectively.