Stop Chasing Perfect Prompts: Build Systems Instead

Description and use

Problem: Lack of explicit scoring rules and error handling in an LLM pipeline caused inconsistent confidence scores and failures with real data. Solution: create a separate scoring rubric, add hard constraints, a dead‑letter queue, centralized configuration, and clear contracts between stages. Result: reduced errors, corrected scores, and faster debugging.

Problem

LLM pipelines lacked explicit scoring rules and error handling, causing inconsistent confidence scores and failures when real data was used.

Outcome

Explicit rule definition and system design reduced dead letter rate from 41% to 11%, corrected confidence scores immediately, and cut debugging time from hours to minutes.

Workflow

• 1. Define a scoring rubric as a separate document outlining evidence weights and thresholds.
• 2. Translate the rubric into a system instruction with hard constraints and explicit consequences.
• 3. Add a dead letter queue to capture failures before feature completion.
• 4. Centralize configuration (thresholds, endpoints) in a single loader.
• 5. Chain outputs between stages with defined contracts (e.g., web search results to scoring node).
• 6. Run real‑data validation before production deployment.
• 7. Instrument the pipeline to measure token usage and costs.
• 8. Iterate on optimization based on measured baselines.

Evidence

We added four lines: what the hint represents, how to cross‑reference it against web search results, how a confirmed match shifts the threshold, and what to do when no hint arrives. Scores corrected immediately.

Source

With it, you see a 41% dead letter rate drop to 11% after a single parser fix, because the queue made the failure visible.

Source