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 →Month: August 2024
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 Security: Understanding Prompt Injection, Jailbreaking, and Attack Vectors (Part 1 of 2)
A comprehensive guide to securing LLM applications against prompt injection, jailbreaking, and data exfiltration attacks. Includes production-ready defense implementations.
Read more →Vector Search Algorithms: From Brute Force to HNSW and Beyond
Introduction: Vector search is the foundation of modern semantic retrieval systems, enabling applications to find similar items based on meaning rather than exact keyword matches. Understanding the algorithms behind vector search—from brute-force linear scan to sophisticated approximate nearest neighbor (ANN) methods—is essential for building efficient retrieval systems. This guide covers the core algorithms that power […]
Read more →Vector Embeddings Deep Dive: From Theory to Production Search Systems
Introduction: Vector embeddings are the foundation of modern AI applications—from semantic search to RAG systems to recommendation engines. They transform text, images, and other data into dense numerical representations that capture semantic meaning, enabling machines to understand similarity and relationships in ways that traditional keyword matching never could. This guide provides a deep dive into […]
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