When retrieval augmented generation is overkill (Hindi)

Discover when retrieval augmented generation is overkill for your product and how to avoid over-engineering AI features. Product managers often default to complex architectures, but building a vector database for small datasets burns budget and delays launch. This lesson breaks down the hidden costs of adding a search step to your workflow. We compare fetching text chunks to the speed of plain prompts. You will learn how context windows and token limits dictate architecture choices, and how prompt caching helps. We provide a framework to evaluate data size and update frequency. By applying the rule of checking if documentation fits in one prompt, you save API tokens. You will also learn when to pivot to fine-tuning for static info instead of building unnecessary infrastructure. In this lesson: - Hidden costs and latency of vector databases - Context windows and token limits explained - The fifty page rule for plain prompts - Evaluating update frequency for architecture U2xAI Academy - AI skills for product managers. यह लेसन हिंदी में है. This lesson is narrated in Hindi.