Optimization

Your Docker images are probably too big. Build tools, dev dependencies, source files—all shipping to production when only the compiled binary matters. Multi-stage builds fix this by separating build environment from runtime environment. The Problem A typical single-stage Dockerfile: 1 2 3 4 5 6 7 8 9 FROM python:3.11 WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . CMD ["python", "app.py"] This image includes: ...

Your Docker images are probably too big. Mine were. Then I learned about multi-stage builds. The problem is simple: build tools bloat production images. You need Node.js and npm to build your React app, but you only need nginx to serve it. You need Go and its toolchain to compile, but the binary runs standalone. Every megabyte of build tooling in your production image is wasted space, slower deploys, and expanded attack surface. ...

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: ...