AI Engineer Portal
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Context Engineering
The meta-skill of 2026: designing the entire information environment an LLM operates in. Goes beyond prompting to manage memory, retrieval, tool results, and context window budgets.
From prompt engineering to context engineering
Why crafting a single prompt is no longer enough, and how context engineering unifies prompting, RAG, memory, and tool use into one design discipline.
From prompt engineering to context engineering
Why crafting a single prompt is no longer enough, and how context engineering unifies prompting, RAG, memory, and tool use into one design discipline.
Context window budgeting
Token counting, priority-based context assembly, and truncation strategies for managing limited context windows in production.
Memory architecture for AI applications
Three-tier memory design, conversation summarization for long sessions, and user preference persistence.
Dynamic context assembly
Task-aware context routing, intent classification to drive context composition, and A/B testing context strategies.
Tool results in context
Formatting tool outputs for LLM comprehension, compressing large tool results, and structuring multi-tool context injection.
Context quality and debugging
Diagnosing when models ignore context, relevance scoring, attention analysis, and common context failure modes.
Production context pipelines
End-to-end context assembly in production: caching, parallel fetching, latency optimization, and quality metrics.