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deepmind.google·6d ago
AI Engineering
Hacker News·29d ago·5 min read
Not everything should cost a token: the case for deterministic AI
TL;DR
Many teams over-rely on AI models for tasks that don't require complex reasoning, leading to high costs and inefficiencies. A recent example showed that a simple data reformatting task was unnecessarily tokenized, resulting in inflated bills and degraded performance.
✦ Why It Matters
Identify and automate simple tasks with scripts instead of using AI to save costs and improve reliability.
Key Takeaways
- Evaluate tasks to determine if they require AI or can be automated with scripts.
- Reduce token usage by avoiding unnecessary AI prompts for simple operations.
- Implement cron jobs for routine tasks instead of relying on AI models.
- Monitor costs associated with AI usage to identify inefficiencies.
Full Summary
Current trends in AI usage often lead teams to treat language models as universal solutions for all tasks, including simple ones. For instance, a team automated a daily job to pull and reformat JSON data using an AI model, which resulted in high token consumption and occasional errors.
This approach bloated the context window with unnecessary data, ultimately degrading the model's reasoning capabilities. The key takeaway is that not all tasks should be tokenized; deterministic solutions, such as scripts or cron jobs, are more efficient for straightforward operations.
By recognizing which tasks can be handled without AI, teams can significantly reduce costs and improve the reliability of their outputs. Engineers should focus on separating deterministic tasks from those that genuinely require AI reasoning.