Stop Using LLMs Like Giant Problem Solvers

2026-05-26 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, short

Summary

An AI engineer successfully transformed 100 messy compliance PDFs into structured JSON rules after an initial "brute force" approach with a single agent proved unreliable due to subtle, hard-to-verify errors. The effective solution involved reframing the problem by making the agent's job smaller. This included preparing source data upfront to reduce retrieval uncertainty, stripping unnecessary metadata, and crucially, processing documents one at a time using parallel subagents. This iterative method enabled easier inspection, retries, and auditing of individual outputs. A key design decision was adding reference IDs to each generated rule, enhancing traceability and auditability. The author concludes that system reliability improved not by making the agent perfect, but by designing a workflow that facilitated output validation and recovery, echoing JJ Geewax's advice to stop using LLMs as giant problem solvers.

Key takeaway

For AI Engineers building scalable and reliable systems that process messy data, resist the urge to treat LLMs as giant problem solvers. Instead, design your workflows to make the agent's job smaller and more focused. You should prepare source data, strip irrelevant context, and process tasks iteratively. Augment the agent's semantic judgment with surrounding code for validation, parallelization, and error recovery, ensuring your outputs are traceable and auditable for production readiness.

Key insights

For reliable, scalable AI systems, break down LLM tasks into smaller units and augment with code for structure, validation, and control.

Principles

• Reduce agent retrieval uncertainty.
• Strip irrelevant context for cleaner reasoning.
• Separate agent's semantic work from code's mechanical tasks.

Method

Prepare source data and strip metadata. Process documents iteratively, one at a time, using parallel subagents. Add reference IDs to generated rules for enhanced auditability and traceability.

In practice

• Temporarily store relevant raw data locally.
• Use scripts to strip unnecessary metadata.
• Add reference IDs to generated rules.

Topics

• LLM Engineering
• AI Agents
• Data Transformation
• System Reliability
• Workflow Design
• Auditing

Best for: AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.