You have been tasked at your organization to manage a large language model (LLM) project.

Large language models (LLMs) excel at generating, understanding, and manipulating text. According to the CPMAI Glossary:

Content summarization is a core NLP function: “the process of using AI/ML techniques to generate a concise overview of a larger body of text.”

Machine translation: “the use of AI to automatically translate text or speech from one language to another.”

Classification: LLMs can assign content to categories via fine-tuned classification heads (“classifier” term), making them suitable for content categorization.

Code generation: As generative AI, LLMs can produce new content, including code snippets, by pattern learning from programming corpora (“generative AI” term).

Search quality improvement: LLMs can rephrase queries, expand keywords, and rank results to enhance search relevance. Though not explicitly detailed in the glossary, this capability derives directly from their generative and understanding strengths.

LLMs are not designed for pure process automation (option A), which is handled by RPA or orchestrators rather than by text-centric models.

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