Introduction: How do you know if your LLM application is actually working? Evaluation is one of the most challenging aspects of building AI systems—unlike traditional software where tests pass or fail, LLM outputs exist on a spectrum of quality. This guide covers the essential metrics, benchmarks, and tools for evaluating LLMs, from automated metrics like […]
Read more →Category: Artificial Intelligence(AI)
Ethical Considerations in Generative AI: Balancing Creativity and Responsibility
The Weight of Responsibility After two decades of building enterprise systems, I have witnessed technology transform industries in ways that seemed impossible when I started my career. But nothing has challenged my understanding of responsible engineering quite like the emergence of generative AI. The systems we build today can create content indistinguishable from human work, […]
Read more →Hallucinations in Generative AI: Understanding, Challenges, and Solutions
The Reality Check We All Need The first time I encountered a hallucination in a production AI system, it cost my client three days of debugging and a significant amount of trust. A customer-facing chatbot had confidently provided detailed instructions for a product feature that simply did not exist. The response was articulate, well-structured, and […]
Read more →LLM Prompt Templates: Building Maintainable Prompt Systems
Introduction: Hardcoded prompts are a maintenance nightmare. When prompts are scattered across your codebase as string literals, updating them requires code changes, testing, and deployment. Prompt templates solve this by separating prompt logic from application code. This guide covers building a robust prompt template system: variable substitution, conditional sections, template inheritance, version control, and A/B […]
Read more →Error Handling in LLM Applications: Retry, Fallback, and Circuit Breakers
Introduction: LLM APIs fail in ways traditional APIs don’t—rate limits, content filters, malformed outputs, timeouts on long generations, and model-specific quirks. Building resilient LLM applications requires comprehensive error handling: retry logic with exponential backoff, fallback strategies when primary models fail, circuit breakers to prevent cascade failures, and graceful degradation for user-facing applications. This guide covers […]
Read more →LLM Rate Limiting and Throttling: Building Resilient AI Applications
Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request caps. Hit these limits and your application grinds to a halt with 429 errors. Worse, aggressive retry logic can trigger longer cooldowns. Proper rate limiting isn’t just about staying under limits; it’s about maximizing throughput while gracefully handling bursts, prioritizing […]
Read more →