Category: Artificial Intelligence(AI)

Introduction: LLM API costs can escalate quickly—a single GPT-4 call costs 100x more than GPT-4o-mini for the same tokens. Effective cost optimization requires a multi-pronged approach: intelligent model routing based on task complexity, aggressive caching for repeated queries, prompt optimization to reduce token usage, and batching to maximize throughput. This guide covers practical cost optimization […]

Introduction: The era of text-only LLMs is ending. Modern vision-language models like GPT-4V, Claude 3, and Gemini can see images, understand diagrams, read documents, and reason about visual content alongside text. This opens entirely new application categories: document understanding, visual Q&A, image-based search, accessibility tools, and creative applications. This guide covers building multi-modal AI applications […]

Introduction: Deploying LLMs in production without guardrails is like driving without seatbelts—it might work fine until it doesn’t. Users will try to jailbreak your system, inject malicious prompts, extract training data, and push your model into generating harmful content. Guardrails are the safety layer between raw LLM capabilities and your users. This guide covers implementing […]

Introduction: Production LLM applications need structured prompt management—not ad-hoc string concatenation scattered across code. Prompt templates provide reusable, parameterized prompts with consistent formatting. Versioning enables A/B testing, rollbacks, and tracking which prompts produced which results. This guide covers practical prompt template patterns: template engines and variable substitution, prompt registries, version control strategies, A/B testing frameworks, […]