Why Treating LLMs as Black-Box Problem Solvers Fails: Lessons from Processing 100 Compliance PDFs
By
Clara Chong
A baker's-dozen of insight crammed into one ring.
Summary
The article discusses the author's experience transforming 100 messy compliance PDFs into structured JSON rules. It critiques the common approach of using LLMs as brute-force problem solvers, showing how initial promising results (valid JSON output) hid deeper issues like overly broad rules, missed nuances, and inaccuracies. The author advocates for a more structured, deterministic approach with validation loops around LLM agents rather than treating them as giant black-box problem solvers.
Key quotes
· 3 pulledThe brute force approach was obvious: give the agent the source text, explain the task, provide examples, and ask it to generate the rules.
At a glance, the output looked fine. The output JSON was valid and matched what I expected.
But as I was manually sampling the results to check for accuracy, the cracks appeared. Some rules were too broad, others were missed. Some rules failed to preserve the nuances of the original text.
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