7 Lessons from Building a Small-Scale AI Application
This article details seven lessons learned from building a small-scale AI assistant over the past year. The author discovered that scalability issues arose earlier than anticipated. AI programming is stochastic, requiring iterative adjustments to prompts, fine-tuning, preference tuning, and hyperparameters. Data quality is crucial, with significant time investment in building and maintaining a high-quality dataset and processing pipeline. Model evaluation is equally important, as simple validation sets often fail to capture real-world edge cases. Trust and quality are paramount, demanding continuous experimentation and evaluation. The training pipeline itself is the core intellectual property, constantly refined through iteration. Finally, the author cautions against over-reliance on AI libraries due to potential incompleteness or poor ecosystem integration; building directly upon lower-level abstractions is often more reliable.
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