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LLMs Are Great... Until You Use Them in Production
- Building with LLMs for fun is different from using them in production, with a wider gap than expected.
- Initial excitement with LLMs made users feel like having superpowers, but challenges arose in real-world applications.
- Inconsistencies in output, including correct vs. incorrect answers, pose a significant challenge in using LLMs reliably.
- Errors in LLM outputs may not always be obvious, requiring additional testing, monitoring, and logic.
- Cost implications of running LLMs in production can escalate rapidly, necessitating careful usage to avoid unexpected expenses.
- Users need to treat LLMs differently from traditional code, employing testing variations, fallback flows, and output verification.
- Caching results for tasks that don't change often can help reduce costs associated with repeated LLM calls.
- Human oversight remains crucial for high-stakes tasks to ensure quality and user trust in LLM-generated content.
- Working with LLMs can be emotionally challenging, balancing excitement with frustration and the need for adaptability.
- Key recommendations for working with LLMs include starting small, planning for inconsistency, monitoring outputs, and staying adaptable.
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