Handle rate limits and transient failures gracefully with exponential backoff.
Read more →Category: Artificial Intelligence(AI)
AWS Bedrock: Building Enterprise Generative AI Applications on AWS
AWS re:Invent 2024 brought significant updates to Amazon Bedrock, and after spending the past month integrating these capabilities into production systems, I want to share what actually matters for enterprise adoption. Having built generative AI applications across multiple cloud platforms over the past two decades, Bedrock represents a meaningful shift in how we can deploy […]
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 […]
Read more →LLM Cost Optimization: Model Routing, Token Reduction, and Budget Management (Part 2 of 2)
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 […]
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