Ai

Every LLM API call costs money and takes time. When users ask variations of the same question, you’re paying for the same computation repeatedly. Semantic caching solves this by recognizing that “What’s the weather in NYC?” and “How’s the weather in New York City?” are functionally identical. The Problem with Traditional Caching Standard key-value caching uses exact string matching: 1 2 3 cache_key = hash(prompt) if cache_key in cache: return cache[cache_key] This fails for LLM applications because: ...

One of the most underestimated challenges in building LLM-powered applications is context window management. You’ve got 128k tokens, but that doesn’t mean you should use them all on every request. The Problem Large context windows create a false sense of abundance. Yes, you can stuff 100k tokens into a request, but you’ll pay for it in: Latency: More tokens = slower responses Cost: You’re billed per token (input and output) Quality degradation: The “lost in the middle” phenomenon is real Practical Patterns 1. Rolling Window with Summary Keep a rolling window of recent conversation, but periodically summarize older context: ...

How to integrate large language models into your applications — from basic API calls to production-ready patterns.

How AI coding assistants transformed my workflow — and why the skeptics are missing out.