BradMoon@xy9301
Two Extreme Misconceptions About Using AI/LLMs: The core issue is cognitive bias regarding engineering investment.
The first group's problem is excessive optimism and overgeneralization. They might happen to get decent results on a particular case and immediately think they've found a "silver bullet," ignoring that these successes might just be because the task happened to fall within the LLM's capability boundaries, they got lucky with a domain well-covered by the model's training data, or they didn't encounter edge cases and various complex real-world scenarios. They don't realize there's a huge gap between "the demo works" and "production-ready."
The second group's problem is premature abandonment and lazy thinking. They treat LLMs like magic wands, expecting perfect results with zero investment. When they discover the need to carefully design prompts, iteratively optimize, and handle various corner cases, they immediately dismiss the tool itself rather than reflect on whether their own investment was insufficient.
The systematic engineering work is what transforms LLMs from "promising tools" into "reliable productivity": 1. Prompt Engineering isn't simply writing a few sentences—it requires repeated experimentation and iteration, including structural design of instructions, careful selection of few-shot examples, explicit constraints on output format, and handling instructions for edge cases. Every detail can impact result quality and stability. 2. Cross-file and cross-contract Context Engineering requires careful design of how to handle dependencies between contracts, inheritance relationships, and cross-contract calls—how to effectively feed this context to the LLM, how to chunk it, how to maintain associations, and how to control token budget. 3. Checklist collection needs to be systematically built based on issues encountered in actual use, known vulnerability patterns, and industry best practices. 4. Regression testing must ensure each optimization doesn't break previously working scenarios, requiring establishment and continuous maintenance of test case sets, building extensive benchmarks, validating effectiveness with real, diverse cases to ensure results are stable, reproducible, and broadly applicable—not accidentally successful.
This is no different from traditional software development—both require rigorous engineering discipline. The problem is that AI marketing rhetoric and some early success stories have created an illusion that "AI is easy to use," leading many to underestimate the professionalism and investment required to use it well.
