AI Workflows Overview Work in Progress
🚧 This section is actively being developed. Content is being added and refined regularly. Check back soon for updates!
Welcome to the AI Workflow section. This area explores how to build, optimize, and deploy robust AI applications, moving beyond simple prompts to complex, reliable systems.
What is an AI Workflow?
An AI workflow is the orchestrated sequence of steps that an application takes to produce a result using Large Language Models (LLMs) or other AI components. Unlike a simple chatbot interaction, a workflow might involve:
- Retrieval Augmented Generation (RAG): Fetching relevant data before asking the model.
- Multi-step Chains: Using the output of one model call as the input for the next.
- Routing: Deciding which path to take based on the user's input.
- Verification: Checking the model's output for accuracy or safety.
Why Structure Matters
LLMs are probabilistic. By structuring them into workflows, we can:
- Increase Reliability: Break complex tasks into smaller, manageable sub-tasks.
- Improve Accuracy: Provide specific context and tools for each step.
- Enable Complex Behaviors: Allow systems to plan, reason, and act.
Contents
- Core Concepts: Understanding the building blocks.
- Workflow Patterns: Common architectural patterns for AI apps.
- AI Agents: Autonomous systems that can pursue goals.
- Tooling: The ecosystem of libraries and frameworks.