Building real-world features with AI can be challenging due to reliability issues. While AI agents are known for great demos, creating reliable products can be tricky. However, there is hope as it is possible to build reliable systems from AI. High-level steps include writing simple prompts, building an eval system, deploying with observability, investing in Retrieval Augmented Generation (RAG), and fine-tuning the model. One breakthrough idea is to use complementary agents for improved reliability. The process outlined is accessible to those without much AI experience but requires strong software engineering skills. By following best practices and incorporating unique strategies, teams can maximize the potential of AI technology.
https://www.rainforestqa.com/blog/building-reliable-systems-out-of-unreliable-agents