LLM applications need failure-first design: 3 patterns for reliable output

This article details a failure-first design approach for LLM applications, drawing lessons from a forensic audit of an options trading system. The author emphasizes that LLM outputs should never be fully trusted and proposes three non-negotiable patterns for applications with real-world side effects. The first pattern involves rigorous schema validation, using tools like Pydantic, to catch malformed JSON, missing fields, incorrect data types, or out-of-range values before they impact downstream processes. The second pattern suggests that while LLM-as-judge can aid in development, human approval is essential for outbound content, with a system scoring memos on dimensions like specificity and grounding. AI

IMPACT Establishes best practices for building robust LLM applications, emphasizing validation and human oversight to prevent costly errors.

RANK_REASON Article discusses design patterns for LLM applications based on past failures, not a new release or significant industry event.

Read on dev.to — LLM tag →

• ADX
• BaseModel
• Field
• JSON
• LLM
• Pydantic

• product
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →