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End-to-End Architecture: Input, Processing, Output, Feedback Loops

Core

Design end-to-end architecture with input, processing, output, and feedback loops · Difficulty 3/5

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end-to-end-architecturefeedback-loopsobservabilityproduction-design

Explanation

A production design is more than a model call. The objective explicitly names the shape an architect must design for: input -> processing -> output -> feedback loops.

The Four Stages

  • Input -- ingestion, validation, and separation of trusted instructions from untrusted data; retrieval of grounding context (Domain 3).
  • Processing -- the chosen pattern (augmented LLM / workflow / agent), model selection, prompt and context assembly.
  • Output -- structured-output contracts, validation, defensive parsing, and delivery to downstream systems.
  • Feedback loops -- evaluation, monitoring, and observability that feed measured quality back into iteration (Domains 4 and 6). Non-determinism makes this loop mandatory, not optional.

Why Feedback Loops Are Not Optional

LLM output is non-deterministic: the same input can produce different outputs across calls, and a model upgrade can silently shift behavior. The feedback loop is what separates an architecture from a demo -- it is how you:

  • Detect regressions on a new model version
  • Catch retrieval drift (grounding context degrading over time)
  • Prove the system still meets its SLA

A design that stops at "output" and has no plan for evaluation or monitoring has not actually finished the architecture -- it has only built the demo described in the production-readiness trap.

Applying the Four Stages

When asked to design an end-to-end architecture, walk through each stage explicitly:

  1. What validates and sanitizes input, and how is untrusted data kept separate from trusted instructions?
  2. What pattern and model handle processing, and how is context assembled?
  3. What contract does the output honor, and what happens if parsing fails?
  4. What observability signal tells you the system is still working correctly next month, after a model version bump or a shift in the underlying data?

This four-stage shape is the mental checklist for any "design the architecture" scenario item -- an answer that only addresses processing is incomplete.

Key Takeaways

  • End-to-end architecture shape: input -> processing -> output -> feedback loops
  • Input stage separates trusted instructions from untrusted data and retrieves grounding context
  • Output stage needs structured-output contracts, validation, and defensive parsing
  • Feedback loops (evaluation, monitoring, observability) are mandatory because LLM output is non-deterministic
  • Feedback loops are how you detect regressions on new model versions and catch retrieval drift

Glossary Terms

Related Concepts

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