Patterns for effectively feeding context to large language models and other AI systems.
Technical deep-dive into designing prompt structures that maximize context utilization within token limits, including retrieval-augmented generation patterns, context compression techniques, and multi-turn conversation management for enterprise applications.
Strategic approaches to deploying large language models at enterprise scale while optimizing the performance-cost tradeoff.
Best practices for connecting AI context systems with enterprise data lake infrastructure for enriched, governed context management.
Comprehensive guide to managing AI models from development through deployment and retirement in enterprise environments.
Strategies for extracting and integrating context from legacy enterprise systems including mainframes, SAP, and custom applications.
A comprehensive framework for evaluating and selecting AI platform vendors that meet enterprise requirements.
