... applications. Why? Because we saw so many businesses excited by LLMs but completely stumped on how to actually implement them. It's a real pain point! Right now, our main focus is helping companies build out their enterprise knowledge bases basically waking up all that dormant data and documentation with AI so it can actually be useful. Somehow, we've already worked on or consulted for 15+ of these knowledge base

... p/bolt-new community, I recently started building Couples Hub ( https://coupleshub.io/ ) a React-based application and Next.js based

... window sizes (now reaching hundreds of thousands of tokens). However, it can become expensive with large inputs and may suffer from context overflow. RAG  reduces token usage by retrieving only relevant snippets, making it efficient for large knowledge bases. However, it requires maintaining an embedding database and tuning retrieval mechanisms. Fine-Tuning  offers the best customization, improving response quality and efficiency. However, it demands significant resources, time, and ongoing model updates. Why We Chose Prompt Context ... ... current needs,  prompt context  was the most practical choice: It allows for a fast development cycle without additional infrastructure. Large context windows (100k+ tokens) are sufficient for our small knowledge base