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# Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs

**[Towards Data Science](https://daily.dev/sources/tds)** · 30 min read · 1 upvotes · 0 comments

## Summary

A deep-dive into building the generation layer of an enterprise RAG pipeline. The core pattern is a dispatcher that takes a typed ParsedQuestion, selects the appropriate answer schema from a registry, and composes a system prompt from a fixed BASE plus shape-specific and constraint-specific fragments — avoiding the 'mega-prompt' anti-pattern. Key topics include: structural hints for scoping retrieval via page numbers in the question, combined vs. sequential chunk processing strategies, per-field evidence wrappers (FieldExtraction[T]) for multi-field extraction with per-field citations, citation verification against source lines, and dynamic few-shot examples retrieved from a validated example bank. The article emphasizes full trace persistence (raw API response, fragments applied, schema used) for auditability and reproducibility.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/assemble-each-rag-generation-prompt-from-a-base-prompt-plus-the-rules-each-question-needs>

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- [The Untaught Lessons of RAG Question Parsing: Structure Before You Search](https://daily.dev/posts/the-untaught-lessons-of-rag-question-parsing-structure-before-you-search-nngi7ml0l) · Towards Data Science · 3 upvotes · 0 comments

---

Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#rag](https://daily.dev/tags/rag), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#pydantic](https://daily.dev/tags/pydantic)

[View this post on daily.dev](https://daily.dev/posts/assemble-each-rag-generation-prompt-from-a-base-prompt-plus-the-rules-each-question-needs-ii7ghlfx9)

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