customer_support · workflow
Assembled scales LLM serving to millions of monthly requests using Go
Serving LLMs in production requires handling unstructured model outputs, managing concurrent API calls with additive latency, and building multi-step response transformations that remain maintainable and testable.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Structured LLM output schema
Go struct tags and reflection automatically generate a well-defined JSON schema for structured LLM output.
Tools used
GoOpenAIgo-openaiPythonscikit-learnsentence-transformerstransformersClaude 3.5 SonnetLlama
Outcome
Assembled's Go-based infrastructure handles millions of monthly LLM requests with minimal performance tuning, using a composable pipeline that makes response transformations maintainable and testable.
Results
Time savedmillions
Volumereduces our total latency to that of the slowest backend
Grounding & classification
Source type: technical build writeup
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