Gemini 2.5 Flash-Lite

High-Performance RAG Integration: Successfully integrated Gemini 2.5 Flash-Lite Preview into the Notion RAG CLI system, achieving ~1.4s average query response times while processing complex document retrieval and generation tasks. Implemented a sophisticated prompt engineering approach centered on simple markdown documentation that explains the codebase structure and retrieval context clearly and concisely. This documentation-first strategy proved more effective than complex prompt engineering systems, allowing Gemini to consistently generate accurate, contextually relevant responses. Built comprehensive cost tracking using tiktoken for accurate token counting, with real-time cost estimation and persistent logging to cost_log.json that enables budget monitoring across extended usage sessions.

Context Window Optimization Challenge: Addressed the challenge of managing large context windows by implementing intelligent context selection and structured formatting strategies. Rather than passing entire document collections, designed a retrieval system that selects the most relevant 3-5 chunks based on semantic similarity, then structures them with clear markdown headers indicating source pages and URLs. Used structured formatting for both input contexts and output responses to ensure the model can efficiently parse information and generate well-formatted answers. This approach balanced comprehensive context provision with API efficiency, maintaining fast response times while ensuring high answer quality.