LLMs are AI models trained on large amounts of text to understand and generate human-like language. Developers use them for applications such as chatbots, code generation, summarization, and question answering.

Prompt engineering is the process of designing effective instructions for an AI model to produce accurate and relevant results. Clear context, specific instructions, examples, and expected output formats generally improve responses.

Retrieval-Augmented Generation (RAG) combines an LLM with external knowledge sources. It retrieves relevant information from documents or databases and provides that context to the model before generating an answer.

Fine-tuning involves training a pre-trained AI model further on a specific dataset or task. It helps adapt the model to specialized requirements, terminology, or response patterns.

Developers can integrate Generative AI through APIs or AI frameworks and connect models with applications, databases, and business systems. Common use cases include AI assistants, content generation, coding tools, and automated support.

Common challenges include inaccurate or hallucinated responses, data privacy, security risks, bias, high computational costs, and unpredictable outputs. Developers should use validation, monitoring, access controls, and reliable data sources to reduce these risks.