Category: Artificial Intelligence(AI)

Introduction: Production LLM applications often benefit from using multiple models—routing simple queries to cheaper models, using specialized models for specific tasks, and falling back to alternatives when primary models fail. Multi-model orchestration enables cost optimization, improved reliability, and access to each model’s unique strengths. This guide covers practical orchestration patterns: model routing based on query… Continue reading

Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost cents and take seconds—multiply that by thousands of users asking similar questions, and costs spiral quickly. Semantic caching solves this by recognizing that “What’s the weather in NYC?” and “Tell me NYC weather” are essentially the same query. Instead of exact… Continue reading

Introduction: Chatbots without memory feel robotic—they forget your name, repeat questions, and lose context mid-conversation. Production chatbots need sophisticated memory systems: short-term memory for the current conversation, long-term memory for user preferences and history, and summary memory to compress long interactions. This guide covers implementing these memory patterns: conversation buffers, vector-based retrieval, automatic summarization, and… Continue reading

Introduction: Google’s Gemini API represents a significant leap in multimodal AI capabilities. Launched in December 2023, Gemini models are natively multimodal, trained from the ground up to understand and generate text, images, audio, and video. With context windows up to 2 million tokens and native Google Search grounding, Gemini offers unique capabilities for building sophisticated… Continue reading

Introduction: Prompt engineering is both art and science—small changes in wording can dramatically affect LLM output quality. Systematic prompt optimization goes beyond trial and error to find prompts that consistently perform well. This guide covers proven optimization techniques: few-shot learning with carefully selected examples, chain-of-thought prompting for complex reasoning, structured output formatting, prompt compression for… Continue reading

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… Continue reading

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… Continue reading

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… Continue reading

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,… Continue reading