Programming Tutorials

  Most AI engineers treat token consumption as a linear operational expense: the more you use, the more you pay. This mindset is obsolete with modern architectures like DeepSeek V3 and R1. If you are building Retrieval Augmented Generation (RAG) systems or SaaS platforms with heavy system prompts, you are likely overpaying for input tokens by an order of magnitude. The bottleneck isn't just the model's pricing per 1M tokens; it is the redundant computation of identical text blocks. By failing to structure prompts for DeepSeek’s disk-based prefix caching, you force the model to re-process static data for every single request. This guide outlines the technical architecture required to leverage DeepSeek’s Context Caching. We will move beyond generic advice and implement a specific prompt structure that forces cache hits, reducing input costs by up to 90% and significantly lowering Time to First Token (TTFT). The Root Cause: Why You Are Breaking the Prefix Match To understand why y...

  You have likely encountered this scenario: You are integrating the DeepSeek API using the OpenAI Python SDK. Your application runs smoothly during testing, but under production load or during peak API hours, requests hang indefinitely. You don't get a standard HTTP 429 "Too Many Requests" error. Instead, your connection eventually dies with a   ReadTimeout   or a generic   ConnectionError . Standard retry logic relies on HTTP status codes. When an API "ghosts" the connection—accepting the TCP handshake but delaying the HTTP response headers—standard error handling fails. This article details why DeepSeek’s load shedding behaves this way and provides a production-grade Python solution using  tenacity  and customized  httpx  transport layers to handle these dynamic limits robustly. The Root Cause: Load Shedding vs. Rate Limiting To fix the issue, we must understand the infrastructure behavior. Most developers confuse  Rate Limiting  with...