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 […]
Read more →Category: Artificial Intelligence(AI)
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 →Generative AI in Healthcare: Revolutionizing Patient Care
The first time I witnessed a generative AI system accurately synthesize a patient’s complex medical history into actionable clinical insights, I understood we were entering a new era of healthcare delivery. After two decades of architecting enterprise systems across industries, I can say that healthcare presents both the greatest challenges and the most profound opportunities […]
Read more →Context Distillation Methods: Extracting Signal from Long Documents
Introduction: Long contexts contain valuable information, but they also contain noise, redundancy, and irrelevant details that consume tokens and dilute model attention. Context distillation extracts the essential information from lengthy documents, conversations, or retrieved passages, producing compact representations that preserve what matters while discarding what doesn’t. This technique is crucial for RAG systems processing multiple […]
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