
This guide introduces startups to building generative AI applications by explaining foundation models, customization techniques like fine-tuning and retrieval-augmented generation, and the key components needed for implementation.


<div><p>This article introduces generative AI application development for startups, covering foundational concepts, customization approaches, and key architectural components.</p><ul><li>Generative AI applications create new content using pre-trained foundation models</li><li>Instruction-tuned models enable simple deployment with prompt engineering and minimal data</li><li>Three customization techniques: instruction-based fine-tuning, domain adaptation, retrieval-augmented generation</li><li>Core components include foundation models, APIs, embedding endpoints, vector databases, ML platforms</li><li>Fine-tuning requires labeled datasets; domain adaptation uses unlabeled domain-specific data</li><li>RAG augments models with information retrieval without updating model weights</li><li>Monitoring, quality metrics, and security tools recommended for production applications</li></ul><p>Part 1 establishes generative AI concepts and patterns; Part 2 maps these to specific AWS services and example architectures.</p></div>


Related articles