The evolution of cloud computing has fundamentally transformed how we architect, deploy, and manage enterprise applications. After spending over two decades in software engineering and solutions architecture, I’ve witnessed this transformation firsthand—from the early days of virtualization to today’s sophisticated cloud-native and multi-cloud ecosystems. This article explores the principles, patterns, and practical considerations that define […]
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.
Prompt Versioning and Management: Bringing Software Engineering Rigor to LLM Development
Introduction: Prompts are code. They determine how your LLM application behaves, and like code, they need version control, testing, and deployment pipelines. Yet many teams treat prompts as afterthoughts—hardcoded strings scattered across the codebase, changed ad-hoc without tracking. This leads to regressions, inconsistent behavior, and difficulty understanding why outputs changed. This guide covers practical prompt […]
Read more →Multi-turn Conversation Design: Building Natural Dialogue Systems with LLMs
Introduction: Multi-turn conversations are where LLM applications become truly useful. Users don’t just ask single questions—they refine, follow up, reference previous context, and expect the assistant to remember what was discussed. Building effective multi-turn systems requires careful attention to context management, history compression, turn-taking logic, and graceful handling of topic changes. This guide covers practical […]
Read more →LLM Output Parsing: Extracting Structured Data from Language Model Responses
Introduction: LLMs generate text, but applications need structured data. Parsing LLM outputs reliably is one of the most common challenges in production systems. The model might return JSON with extra text, miss required fields, use unexpected formats, or hallucinate invalid values. This guide covers practical parsing strategies: using structured output modes, building robust parsers with […]
Read more →Advanced Retrieval Strategies for RAG: From Query Transformation to Multi-Stage Pipelines
Introduction: Retrieval is the foundation of RAG systems. Poor retrieval means irrelevant context, which leads to hallucinations and wrong answers regardless of how capable your LLM is. Yet many RAG implementations use naive approaches—single-stage vector search with default settings. This guide covers advanced retrieval strategies: query transformation techniques, hybrid search combining dense and sparse methods, […]
Read more →LLM Application Monitoring: Metrics, Tracing, and Alerting for Production AI Systems
Introduction: LLM applications fail in ways traditional software doesn’t. A model might return syntactically correct but factually wrong responses. Latency can spike unpredictably. Costs can explode without warning. Token usage varies wildly based on input. Traditional APM tools miss these LLM-specific failure modes. This guide covers comprehensive monitoring for LLM applications: tracking latency, tokens, and […]
Read more →