5 Things Every Production LLM App Needs Beyond a Good Prompt
camerongreen95 · reddit · 2026-11-15
A Reddit user lays out five engineering pillars that separate production LLM applications from demos, arguing that generating a response is only one part of the build:
- Retrieval architecture: basic vector search often isn't enough; combining keyword retrieval, semantic search, and reranking makes a significant difference on complex documents.
- Tool execution: wiring tools is easy; ensuring correct tool calls, valid arguments, and failure recovery is the real challenge.
- Persistent memory: conversation history isn't the same as persistent memory—apps need to retain useful info across sessions without hauling unnecessary context.
- Guardrails and recovery: tool failures, invalid responses, and unexpected inputs must be designed for, not handled as afterthoughts.
- Evaluation and observability: you need to know why a result was produced, where retrieval failed, and how changes affect performance.
The author is also running a hands-on workshop on November 28 covering hybrid RAG, tools, memory, guardrails, and evals.
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