Introduction: AWS re:Invent 2023 delivered transformative announcements for enterprise AI adoption, with Amazon Bedrock reaching general availability and Amazon Q emerging as AWS’s answer to AI-powered enterprise assistance. These services represent AWS’s strategic vision for making generative AI accessible, secure, and enterprise-ready. After integrating Bedrock into production workloads, I’ve found its model-agnostic approach and native […]
Read more →Category: Technology Engineering
Technology Engineering
Guardrails and Safety for LLMs: Building Secure AI Applications with Input Validation and Output Filtering
Introduction: Production LLM applications need guardrails to ensure safe, appropriate outputs. Without proper safeguards, models can generate harmful content, leak sensitive information, or produce responses that violate business policies. Guardrails provide defense-in-depth: input validation catches problematic requests before they reach the model, output filtering ensures responses meet safety standards, and content moderation prevents harmful generations. […]
Read more →Rate Limiting for LLM APIs: Token Buckets, Queues, and Adaptive Throttling
Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Exceeding these limits results in 429 errors that can cascade through your application. Effective rate limiting on your side prevents hitting API limits, provides fair access across users, and enables graceful degradation under load. This guide covers practical rate […]
Read more →LLM Batch Processing: Scaling AI Workloads from Hundreds to Millions
Introduction: Processing thousands or millions of items through LLMs requires different patterns than single-request applications. Naive sequential processing is too slow, while uncontrolled parallelism hits rate limits and wastes money on retries. This guide covers production batch processing patterns: chunking strategies, parallel execution with rate limiting, progress tracking, checkpoint/resume for long jobs, cost estimation, and […]
Read more →Building Production AI Applications with .NET 8 and C# 12
When .NET 8 and C# 12 were released, I was skeptical. After 15 years building enterprise applications, I’d seen framework updates come and go. But this release changed everything for AI development. Let me show you how to build production AI applications with .NET 8 and C# 12—using actual C# code, not Python wrappers. Figure […]
Read more →LLM Output Formatting: JSON Mode, Pydantic Parsing, and Template-Based Outputs
Introduction: LLM outputs are inherently unstructured text, but applications need structured data—JSON objects, typed responses, specific formats. Getting reliable structured output requires careful prompt engineering, output parsing, validation, and error recovery. This guide covers practical output formatting techniques: JSON mode and structured outputs, Pydantic-based parsing, format enforcement with retries, template-based formatting, and strategies for handling […]
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