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 →Month: October 2024
LLM Cost Optimization: Reducing API Spend Without Sacrificing Quality (Part 1 of 2)
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 →Building AI Agents with LangGraph and CrewAI: A Practical Guide
Learn to build production AI agents using LangGraph and CrewAI. Covers agent architectures, multi-agent teams, tool integration, and production best practices.
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 →LLM Observability: Cost Tracking and Quality Monitoring (Part 2 of 2)
Introduction: You can’t improve what you can’t measure. LLM applications are notoriously difficult to debug—prompts are opaque, responses are non-deterministic, and failures often manifest as subtle quality degradation rather than crashes. Observability gives you visibility into every LLM call: what prompts were sent, what responses came back, how long it took, how much it cost, […]
Read more →Tips and Tricks – Accelerate Pandas with PyArrow Backend
Switch to PyArrow-backed DataFrames for faster operations and lower memory usage.
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