LLM Integrations in Practice: Architecture Patterns, Pitfalls, and Anti-PatternsHow to integrate large language models into real systems without creating fragile, expensive messes
Integrating LLMs into production systems is an engineering problem, not a demo exercise. This post covers proven integration patterns, common mistakes, and what not to build with LLMs.
Quickstart: Build a Memory-Enabled AI Assistant with RAG in a WeekendA minimal architecture that scales: ingestion, retrieval, conversation state, and observability.
Follow a practical quickstart to create a memory-enabled AI assistant using RAG, including ingestion, indexing, conversation state, caching, and basic monitoring.
RAG 101 for AI Engineers: From Naive Retrieval to Production-Grade PipelinesChunking, embeddings, reranking, citations, evaluation, and failure modes explained simply.
A step-by-step guide to building a reliable RAG system, covering chunking, embeddings, retrieval, reranking, context windows, and evaluation tactics for better answers.
RAG in the Real World: Handling Fresh Data, Conflicts, and Source TrustWhat breaks in production and how to fix it with metadata, ranking, and policy.
Discover how to operate RAG systems with changing documents, conflicting sources, and varying trust levels using metadata filters, ranking, citations, and governance.
Understanding Amazon Bedrock Fundamentals: A Complete Guide for DevelopersMaster the core concepts, architecture patterns, and essential components that power Amazon Bedrock
Explore Amazon Bedrock fundamentals including architecture, agent lifecycle management, and core components to build robust AI-driven applications efficiently.