Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just describing what to do, the model can actually do it—query databases, call APIs, execute code, and interact with external systems. OpenAI’s function calling (now called “tools”) and similar features from Anthropic and others let you define available functions, and the model […]
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LLM Rate Limiting: Maximizing API Throughput Without Getting Throttled
Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Hit these limits and your application grinds to a halt with 429 errors. Effective rate limiting isn’t just about staying under limits; it’s about maximizing throughput while maintaining reliability. This guide covers practical rate limiting patterns: token bucket algorithms […]
Read more →Quantization Methods for LLMs: GPTQ, AWQ, and BitsAndBytes
Last year, I needed to run a 13B parameter model on a 16GB GPU. Full precision required 52GB. After testing GPTQ, AWQ, and BitsAndBytes, I reduced memory to 7GB with minimal accuracy loss. After quantizing 30+ models, I’ve learned which method works best for each scenario. Here’s the complete guide to LLM quantization. Figure 1: […]
Read more →Enterprise Machine Learning in Production: Healthcare and Financial Services Case Studies
Real-world enterprise ML implementations in healthcare diagnostics and financial fraud detection. Explore RAG and LLM integration patterns, ML maturity frameworks, and strategic recommendations for building ML-enabled organizations.
Read more →LLM Security: Defense Patterns for Production Applications (Part 2 of 2)
Introduction: LLM applications face unique security challenges—prompt injection, data leakage, jailbreaking, and harmful content generation. Traditional security measures don’t address these AI-specific threats. This guide covers defensive techniques for production LLM systems: input sanitization, prompt injection detection, output filtering, rate limiting, content moderation, and audit logging. These patterns help you build LLM applications that are […]
Read more →LLM Latency Optimization: Techniques for Sub-Second Response Times
Introduction: LLM latency is the silent killer of user experience. Even the most accurate model becomes frustrating when users wait seconds for each response. The challenge is that LLM inference is inherently slow—autoregressive generation means each token depends on all previous tokens. This guide covers practical techniques for reducing perceived and actual latency: streaming responses […]
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