KodeKloud

How AI Workflows Really Work (Part 1/2)

The video introduces AI workflows, systems where developers stitch together large language model (LLM) calls and external tools using predefined code paths. Unlike ad‑hoc prompting, the developer explicitly defines each step, letting the LLM handle heavy lifting while the surrounding logic controls execution. Two core patterns are highlighted. The sequential pattern breaks a task into ordered stages—e.g., one LLM drafts an email, a second refines it—allowing programmatic validation between calls. The routing pattern first classifies the user’s request, then dispatches it to the most suitable handler, enabling model selection based on difficulty. Concrete examples include an email‑draft‑and‑review loop and a classifier that routes simple queries to a lightweight model while reserving a premium model for complex travel planning. The presenter emphasizes that developers can embed checks, such as schema validation, before feeding output to the next LLM. By structuring LLM interactions as deterministic workflows, teams gain predictability, easier debugging, and cost control. Enterprises can scale AI services while balancing performance and expense, making workflow design a strategic capability in the emerging AI stack.