Fast Facts
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The article introduces a modular, bounded-iteration workflow for enterprise document QA, combining multiple patterns (like TOC retrieval, listing aggregation, and cross-reference resolution) into a single orchestrated pipeline that decides which patterns to activate and when to loop, ensuring transparency and control.
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Instead of relying on an agentic LLM to autonomously decide the next steps, the system uses a code-based dispatcher to explicitly select patterns based on parsed questions and document profiles, maintaining reproducibility, auditability, and maintainability critical for enterprise use.
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Central to the pipeline is a feedback loop mechanism that critiques generated answers using structured signals—from the LLM itself, deterministic checks, or external validation—triggering targeted retries with bounded iterations, thus avoiding unbounded loops and drift from the original question.
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The architecture emphasizes control in code over control in the LLM, highlighting that while feedback-driven (structured) RAG mimics some aspects of autonomous agents, true enterprise readiness favors a deterministic, reviewable dispatcher pattern that encodes team knowledge and simplifies debugging and updates.
Understanding RAG Workflow and Looping
Retrieval-Augmented Generation (RAG) systems combine document retrieval with AI-generated answers. To handle different question types, the system runs multiple patterns simultaneously. For example, when asked, “What are all the categories under GOVERN?” it fires three patterns: locating the right section, listing all categories, and synthesizing the answer. Each pattern works independently, with its own trigger and process. But questions often involve multiple patterns at once, making it tricky to decide when to stop looping. Without a clear method, the system risks running endlessly or producing incomplete answers. That’s why an explicit decision point is essential. Today, many rely on models to determine when to stop, but code-based workarounds provide more control and transparency. By defining bounded loops and clear rules, the system can dynamically decide when enough information has been gathered, reducing errors and improving reliability.
Create a Clear, Readable Control System
Instead of leaving iteration decisions to AI models alone, this approach favors a controllable, code-based dispatcher. Think of the dispatcher as a traffic controller that reads the parsed question and document profile, then activates relevant patterns. It assigns limits to how many times each pattern can run, ensuring the pipeline remains efficient. For instance, if a question triggers three patterns, the dispatcher manages their sequence and monitors feedback after each pass. It reads structured responses—like whether the context is complete or answers are satisfactory—and then decides whether to repeat certain patterns or move forward. This setup provides transparency because engineers can read and update the rules easily. By maintaining the feedback mechanics and loop bounds in code, the system stays manageable, debuggable, and adaptable to new question types.
Managing Feedback and Loop Boundaries
One of the key innovations is turning answer critiques into feedback signals. Unlike traditional systems that run patterns once, this pipeline treats the answer as provisional. If the generated answer signals incompleteness—for example, citing “Table 3” that wasn’t retrieved—the system triggers targeted re-retrieval or re-parsing. These feedback rails form big loops crossing multiple patterns, while each pattern also has smaller internal iterations. To prevent infinite looping, the system enforces strict bounds, such as a maximum of four iterations. It also checks for signals like unchanged retrieval results, decreasing confidence, or drifting focus. If any of these occur, the loop stops, and the best answer so far is returned. Importantly, every iteration’s steps are logged in an audit trail, ensuring transparency and compliance. This structure ensures the pipeline adapts intelligently, only re-running what’s necessary, and keeps the entire process predictable and auditable.
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