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 […]
Read more →Category: Technology Engineering
Technology Engineering
Anthropic Claude SDK: Building AI Applications with Advanced Reasoning and 200K Context
Introduction: Anthropic’s Claude SDK provides developers with access to one of the most capable and safety-focused AI model families available. Claude models are known for their exceptional reasoning abilities, 200K token context windows, and strong performance on complex tasks. The SDK offers a clean, intuitive API for building applications with tool use, vision capabilities, and […]
Read more →AI Agent Architectures: From ReAct to Multi-Agent Systems – A Complete Guide
AI agents represent a paradigm shift from simple prompt-response interactions to autonomous systems capable of planning, reasoning, and taking actions. Understanding the architectural patterns that power these agents is essential for building production-grade AI applications. ℹ️ KEY INSIGHT The evolution from chatbots to agents mirrors the transition from procedural to agentic computing – where AI […]
Read more →Structured Output Generation: Reliable JSON from Language Models
Introduction: LLMs generate text, but applications need structured data—JSON objects, database records, API payloads. Getting reliable structured output from language models requires more than asking nicely in the prompt. This guide covers practical techniques for structured generation: defining schemas with Pydantic or JSON Schema, using constrained decoding to guarantee valid output, implementing retry logic with […]
Read more →Prompt Optimization: From Few-Shot to Automated Tuning
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 […]
Read more →Model Context Protocol (MCP): Building AI-Tool Integrations That Scale
Introduction: The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables AI assistants to securely connect with external data sources and tools. Think of MCP as a universal adapter that lets AI models interact with your files, databases, APIs, and services through a standardized interface. Instead of building custom integrations for […]
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