How I Made AI Worse By Trying To Make It Better
AI Summary
Summary of AI Extraction Tool Development
Introduction
- Topic: The speaker discusses their experience of trying to build a complex PDF extraction tool and the lessons learned.
Bitter Lessons
- Macro Bitter Lesson: Investing effort in tweaking AI models often leads to diminishing returns. Focus on improving AI models overall rather than short-term fixes.
- Micro Bitter Lesson: Overengineering systems can lead to brittleness and inefficiency.
Key Experience
- Attempted to create an extraction tool for PDFs with approximately 3,000 lines of code, leading to frustration and inefficiency.
- Initially explored various AI tools (ChatGPT, Claude, Gemini, Llama) for text extraction from PDFs, but found their accuracy lacking due to insufficient prompt specificity.
Overengineered System
- Described the complex system created, which included:
- Multiple parsing attempts with different tools.
- A complicated workflow with three to four AI prompts to achieve desired data extraction.
- Issues with accuracy due to varying PDF formats leading to brittle system performance.
Second Attempt
- Shift to AI-first approach:
- Simplified the system significantly by leveraging AI (particularly Gemini) for extracting data directly from PDFs, resulting in only about 1,200 lines of code.
- Achieved significant improvements:
- Cost: 99.6% savings using Gemini compared to Llama.
- Accuracy: Improved extraction accuracy to around 99% for unknown formats.
- Speed: Reduced processing time significantly.
Conclusion
- Emphasized the importance of asking whether AI is capable of performing specific tasks before embarking on complex system builds.
- Discussed future trends in AI, such as evolving prompt engineering and the potential of AI to guide user interactions instead of the other way around.