Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs
Introduces a modular dispatcher for Enterprise Document Intelligence that replaces monolithic "mega-prompts" with dynamic, shape-specific prompt composition. Utilizes a `ParsedQuestion` schema to extract `expected_answer_shape`, `generation` constraints, and `keywords`, enabling precise routing to typed schemas via `ANSWER_REGISTRY`. Combines a fixed base system prompt with modular fragments based on the question brief, ensuring scalability and maintainability without combinatorial explosion. In
Analysis
TL;DR
- Introduces a modular dispatcher for Enterprise Document Intelligence that replaces monolithic "mega-prompts" with dynamic, shape-specific prompt composition.
- Utilizes a
ParsedQuestionschema to extractexpected_answer_shape,generationconstraints, andkeywords, enabling precise routing to typed schemas viaANSWER_REGISTRY. - Combines a fixed base system prompt with modular fragments based on the question brief, ensuring scalability and maintainability without combinatorial explosion.
- Integrates tightly with previous pipeline stages (document parsing, question parsing, retrieval) to pass structured context like
RetrievalQueryandGenerationBriefdirectly to the generation model.
Why It Matters
This approach addresses a critical scalability bottleneck in enterprise RAG systems where prompt engineering often becomes unmanageable due to complex, hardcoded conditional logic. By decoupling prompt assembly from the model call and relying on structured metadata from earlier pipeline stages, developers can add new answer types or formatting constraints with minimal code changes, significantly reducing maintenance overhead and token waste.
Technical Details
- Dispatcher Architecture: The core component receives a
ParsedQuestionobject, queries theANSWER_REGISTRYto select the appropriate schema based onexpected_answer_shape, and dynamically composes the system prompt by merging a staticBASEwith specific fragments required by the question's constraints. - Structured Input Schema: The
ParsedQuestionmodel includes nested Pydantic objects such asKeyword(with weight and source),RetrievalQuery(for scope and anchors), andGenerationBrief(containing format constraints and disambiguation rules), providing rich context beyond simple text. - Prompt Composition Strategy: Avoids the "mega-prompt" anti-pattern by injecting only relevant constraints (e.g., ISO 8601 for dates, ISO 4217 for amounts) rather than loading all possible rules into every inference call, thereby saving tokens and reducing hallucination risks from irrelevant instructions.
- Trace Persistence: The dispatcher captures the full raw response and the complete prompt history (system + user) on a trace, facilitating debugging and validation in subsequent steps (Article 8C).
- Integration with Retrieval: Leverages
structural_hintsfrom the question parsing stage to scope retrieval operations, allowing natural language pointers (e.g., "on page 1") to filter the search space without explicit pipeline flags.
Industry Insight
- Modular Prompt Engineering: Treat prompt templates as composable modules rather than static strings. This allows teams to iterate on specific formatting rules or answer shapes independently, similar to microservices architecture.
- Schema-Driven Pipelines: Invest heavily in the quality of intermediate structured representations (like
ParsedQuestion). The richer the metadata passed between pipeline stages, the more precise and efficient the final generation step becomes. - Maintainability over Complexity: As enterprise RAG systems grow, avoid adding conditional clauses to a single prompt. Instead, use a registry-based dispatch mechanism to keep the codebase clean and prevent "prompt rot" where instructions become contradictory or forgotten.
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