Prompt bloat: causes, costs & fixes for LLM apps

Prompt bloat in large language model (LLM) applications refers to the excessive size of prompts that can slow down models, increase costs, and degrade performance by overloading the context window with unnecessary information. It's an architectural issue that arises when prompts become cluttered with system instructions, conversation history, and irrelevant tool definitions, leading to increased token usage. This can result in higher costs, longer latency, and quality drift as the model struggles to prioritize relevant information. The article suggests adopting a context-engine approach, which involves dynamically managing and filtering the information presented to the model, rather than simply increasing the context window size. Redis Iris is highlighted as a real-time context engine that offers tools such as vector search, semantic caching, and agent memory to efficiently manage context, aiming to optimize LLM performance by delivering the right information at the right time while keeping costs down.