Prevent excessive API calls by debouncing user input in search fields.
Read more →Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
LLM Cost Optimization: Reducing API Spend Without Sacrificing Quality
Introduction: LLM API costs can spiral quickly—a chatbot handling 10,000 daily users at $0.01 per conversation costs $3,000 monthly. Production systems need cost optimization without sacrificing quality. This guide covers practical strategies: semantic caching to avoid redundant calls, model routing to use cheaper models when possible, prompt compression to reduce token counts, and monitoring to […]
Read more →LLM Evaluation: Metrics, Benchmarks, and A/B Testing
Introduction: Evaluating LLM outputs is challenging because there’s often no single “correct” answer. Traditional metrics like BLEU and ROUGE fall short for open-ended generation. This guide covers modern evaluation approaches: automated metrics for specific tasks, LLM-as-judge for quality assessment, human evaluation frameworks, A/B testing in production, and building comprehensive evaluation pipelines. These techniques help you […]
Read more →Tips and Tricks – Parallelize CPU-Bound Work with ProcessPoolExecutor
Bypass the GIL and utilize all CPU cores for compute-intensive tasks.
Read more →Tips and Tricks – Accelerate Pandas with PyArrow Backend
Switch to PyArrow-backed DataFrames for faster operations and lower memory usage.
Read more →.NET AI Performance Optimization: Reducing Latency and Costs
Last year, I inherited a .NET AI application that was struggling. Response times averaged 2.3 seconds, costs were spiraling, and users were complaining. After three months of optimization, we cut latency by 87% and reduced costs by 72%. Here’s what I learned about optimizing .NET AI applications for production. Figure 1: .NET AI Performance Optimization […]
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