A Survey of Context Engineering for Large Language Models
by Mei et al. (arXiv)
1,400+ papers distilled into a taxonomy for engineering everything that fills an LLM's context.
Overview
A Survey of Context Engineering for Large Language Models (arXiv:2507.13334, submitted July 17, 2025) by Lingrui Mei and colleagues analyzes 1,411 research papers to build a unified taxonomy of context engineering. It decomposes the discipline into foundational components — context retrieval and generation, context processing, and context management — and shows how they compose into system implementations including retrieval-augmented generation (RAG), memory systems, tool-integrated reasoning, and multi-agent architectures. A central finding is a 'fundamental asymmetry': modern models are strong at understanding complex contexts but comparatively weak at generating equally sophisticated long-form outputs, an insight the authors argue should guide real-world system design. The paper serves as a foundational reference and reading map for practitioners moving beyond ad-hoc prompting toward disciplined context design.
At a Glance
- Topic
- Agentic
- Level
- Advanced
- Format
- Paper
- Cost
- Free
- Duration
- ~long-form survey
- Provider
- Mei et al. (arXiv)
- Hands-on
- No
- Certificate
- None
What You’ll Learn
- ✓A precise taxonomy of context engineering: retrieval/generation, processing, and management
- ✓How RAG, memory, tool-integrated reasoning, and multi-agent systems fit a shared framework
- ✓Design trade-offs for assembling and managing what fills an LLM's context window
- ✓The 'understanding vs. generation' asymmetry and what it implies for system design
- ✓A curated map into 1,400+ underlying papers for deeper study
Highlights
- •Synthesizes over 1,411 papers into one coherent taxonomy
- •Covers the full pipeline from retrieval through multi-agent orchestration
- •A go-to reference framing the shift from prompt engineering to context engineering
Who It’s For
Best For
- ✓Engineers designing RAG, memory, or multi-agent LLM systems
- ✓Researchers surveying the context-engineering landscape
- ✓Tech leads defining a principled approach to context design
Prerequisites
- •Solid understanding of LLMs and transformers
- •Familiarity with RAG and agent architectures
FAQ
What is A Survey of Context Engineering for Large Language Models?
A comprehensive 2025 survey that formalizes 'context engineering' — the systematic design of what goes into an LLM's context window — synthesizing over 1,400 papers. It is for engineers and researchers building RAG, memory, tool-use, and multi-agent systems who want a structured map of the field.
Is A Survey of Context Engineering for Large Language Models free?
A Survey of Context Engineering for Large Language Models is free to access.
What level is A Survey of Context Engineering for Large Language Models for?
A Survey of Context Engineering for Large Language Models is aimed at a advanced audience. Recommended background: Solid understanding of LLMs and transformers, Familiarity with RAG and agent architectures.
How long does A Survey of Context Engineering for Large Language Models take?
Expect roughly ~long-form survey. Most learners work through it at their own pace.
What will I learn from A Survey of Context Engineering for Large Language Models?
You'll learn: A precise taxonomy of context engineering: retrieval/generation, processing, and management; How RAG, memory, tool-integrated reasoning, and multi-agent systems fit a shared framework; Design trade-offs for assembling and managing what fills an LLM's context window; The 'understanding vs. generation' asymmetry and what it implies for system design; A curated map into 1,400+ underlying papers for deeper study.