An LLM feature in production
The idea: In production the model is one piece of a pipeline: input guardrails → prompt assembly (system + retrieved context + user input) → model → validate/parse → output guardrails → your app. Every step can fail and must be handled.
What you'll be able to do: You can describe the anatomy of a production LLM feature and why the model is just one component.
The problem it solves: A prompt that works in the playground falls apart in production. What's missing?
Builds on: RAG: retrieval as a callback to similarity
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