Design an LLM Processing Chain

LLM chains connect multiple model calls in sequence, where the output of one step becomes the input of the next. This pattern is fundamental to tasks too complex for a single prompt: document summarisation then sentiment analysis, data extraction then schema validation, draft generation then editorial review. The chain builder lets you define each step with its own model, system prompt snippet, and transformation logic, then preview the full pipeline before writing code. Each node in the chain has an input schema (what it expects), a transformation (the model call), and an output schema (what it produces). Explicit schemas at each boundary prevent type mismatches from propagating silently through the chain and make debugging much faster when something goes wrong mid-pipeline. Adding a validation step between each LLM call adds minimal latency but catches malformed outputs before they corrupt downstream steps. This example chains three steps: first a classifier that categorises an incoming support ticket, then a retrieval step that selects the relevant knowledge base section, and finally a response generator that drafts a reply using the classification and retrieved context. Running the chain on a real ticket and inspecting intermediate outputs is the fastest way to identify which step is causing quality issues.

Step 1: Classify support ticket
  Model: gpt-4o-mini
  Input: raw ticket text
  Output: { category: string, urgency: "low"|"medium"|"high" }

Step 2: Retrieve knowledge base section
  Input: category from Step 1
  Output: { article_title: string, content: string }

Step 3: Generate customer reply
  Model: gpt-4o
  Input: ticket text + retrieved article
  Output: drafted reply (≤ 150 words)