Constraint-Driven Context Engineering: Designing Domain Interfaces for AI Systems

arXiv:2609.27354 · cs.SE, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.SE, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Constraint-Driven Context Engineering — 20× in this paper
• Verification — 13× in this paper · explained in 2 episodes
• Provenance — 9× in this paper · explained in 4 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Domain-Driven Design — 5× in this paper
• Human-in-the-Loop — 4× in this paper · explained in 3 episodes
• RAG — 4× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Attribute-Driven Design — 3× in this paper
• Retrieval-Augmented Generation — 3× in this paper
• Causal Discovery — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• National Health and Medical Research Council — 2× in this paper
• Royal Australian College of General Practitioners — 2× in this paper
• Abstraction — 1× in this paper · explained in 1 episode
• Accountability — 1× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Agent Harness — 1× in this paper · explained in 2 episodes
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes

Sources

• A Survey of Context Engineering for Large Language Models
• LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain
• LLM-as-Judge in Education: A Curriculum-Grounded Marking Pipeline
• ReAct: Synergizing Reasoning and Acting in Language Models

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