Engineering Resources
Practical field guides, architecture references, checklists, and templates for engineers building production systems.
Field Guide 01 · Free PDF
Context Engineering
What should your AI actually see?
A compact field guide to building the context your RAG systems and AI agents receive at every model call: what to retrieve, what to prune, what to carry forward as structured state, and how to prove a smaller context is still sufficient.
What it covers
- Long-context limits and Lost in the Middle
- Context selection and prioritization
- Dynamic pruning and compression
- Structured agent state
- Context budgets and caching
- Worked example and production checklist
10-page PDF · 201 KB · Free, no account or email required
Related reading
Read alongside Context Engineering
Essay
Designing the Context Layer for Production AI Agents
Production AI agents need a context layer that assembles evidence, preserves authority and freshness, and separates model reasoning from durable workflow state.
Guide
Agent Memory vs Execution State
Separate reusable information from the durable state that records progress, tool effects, retries and ownership in an AI agent run.
Guide
AI Agent Architecture
A production AI agent architecture separates model decisions from context, tools, durable execution, permissions, evaluation and observability.
Essay
RAG in Production Is Mostly Not About Retrieval
Most RAG discussions obsess over retrieval. In production, the harder problems are ingestion, data lifecycle, generation control and evaluation.
Elsewhere in Learn
- Essays →
In-depth engineering thinking: how production systems fail, scale and recover.
- Topics →
Connected reading paths through agents, RAG and LLM infrastructure.
- Start Here →
A guided entry point if you are new to ArchCrux.