Introduction: Observability is essential for production LLM applications—you need visibility into latency, token usage, costs, error rates, and output quality. Unlike traditional applications where you can rely on status codes and response times, LLM applications require tracking prompt versions, model behavior, and semantic quality metrics. This guide covers practical observability: distributed tracing for multi-step LLM […]
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LLM Evaluation Metrics: Automated Testing, LLM-as-Judge, and Human Assessment for Production AI
Introduction: Evaluating LLM outputs is fundamentally different from traditional ML evaluation. There’s no single ground truth for creative tasks, quality is subjective, and outputs vary with each generation. Yet rigorous evaluation is essential for production systems—you need to know if your prompts are working, if model changes improve quality, and if your system meets user […]
Read more →Fine-Tuning Large Language Models: A Complete Guide to LoRA and QLoRA
Master parameter-efficient fine-tuning with LoRA and QLoRA. Learn how to customize LLMs like Llama 3 and Mistral on consumer hardware with step-by-step implementation guides.
Read more →Text-to-SQL with LLMs: Building Natural Language Database Interfaces
Introduction: Natural language to SQL is one of the most practical LLM applications. Business users can query databases without knowing SQL, analysts can explore data faster, and developers can prototype queries quickly. But naive implementations fail spectacularly—generating invalid SQL, hallucinating table names, or producing queries that return wrong results. This guide covers building robust text-to-SQL […]
Read more →AWS DevOps and Infrastructure as Code: CDK, CloudFormation, Terraform, and CI/CD (Part 6 of 6)
Infrastructure as Code (IaC) enables you to manage AWS resources through code, providing version control, repeatability, and collaboration. This guide compares AWS CDK, CloudFormation, and Terraform with production-ready examples. 📚 AWS FUNDAMENTALS SERIES – FINAL PART This is the final part of a 6-part series covering AWS Cloud Platform. Part 1: Fundamentals Part 2: Compute […]
Read more →Platform Engineering: Building Internal Developer Platforms
Platform Engineering is the discipline of building self-serve internal platforms that abstract infrastructure complexity from development teams. Unlike traditional DevOps (where developers manage infrastructure), Platform Engineering provides golden paths—opinionated, pre-configured solutions that reduce cognitive load. Gartner predicts 80% of large enterprises will have platform teams by 2026. This guide covers architecture patterns, technology choices, and […]
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