Modern AI applications rarely rely on a single LLM call. Instead, they chain multiple steps like embedding a query, searching a vector database, and generating a response. Each stage depends on the previous output, creating a sequential workflow. The article demonstrates this pattern using Flashq, a job queue library that manages these dependencies.

The code example uses a Queue and Worker from Flashq to orchestrate the pipeline. An `embed` job uses OpenAI's `text-embedding-3-small` model. A `search` job then runs only after the embedding is complete, using the `dependsOn` property. Finally, a `generate` job waits for the search results, providing them as context to a GPT-4 completion.

This approach provides clear error handling; a failed job stops the entire pipeline. It's ideal for RAG systems, document processing, and complex content generation. The pattern ensures data flows correctly between steps, which is critical for building reliable, production-ready AI applications.