Developing generative AI applications requires deep technical knowledge and significant patience, as the journey is fraught with complexities and unexpected challenges.
Many LLMs generate outputs that sound plausible but may contain inaccuracies and hallucinations, particularly when applied to complex datasets, which can lead to misleading results.
Effective prompt engineering can help mitigate hallucinations in LLM outputs, but developers must still actively engage in reviewing and validating the generated content.
A major challenge in generative AI is the absence of reliable confidence scores, which forces developers to create their own methods for assessing and validating the outputs.
#generative-ai #machine-learning #natural-language-processing #development-challenges #data-accuracy
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