Context Engineering

aid: context-engineering
url: https://raw.githubusercontent.com/api-evangelist/context-engineering/refs/heads/main/apis.yml
name: Context Engineering
kind: topic
description: Context engineering is the practice of curating the information that large language models receive at inference
  time so that the model can perform a task reliably and cost-effectively. It treats the context window as a finite attention
  budget and looks for the smallest set of high-signal tokens that maximize the likelihood of the desired outcome. Context
  engineering subsumes and extends prompt engineering, system prompts, tool design, retrieval, agent loops, structured note
  taking, compaction, and multi-agent decomposition. It is a foundational discipline for building production AI agents and
  assistants.
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
tags:
- Agents
- AI
- Anthropic
- Compaction
- Context Window
- LLM
- Memory
- Prompt Engineering
- RAG
- Tools
created: '2025-01-01'
modified: '2026-04-28'
specificationVersion: '0.19'
apis:
- aid: context-engineering:anthropic-guide
  name: Effective Context Engineering for AI Agents
  description: Anthropic's engineering guide to context engineering, framing context as a finite attention budget and walking
    through system prompts, tool design, few-shot examples, just-in-time retrieval, compaction, structured note taking, and
    multi-agent architectures.
  humanURL: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
  baseURL: https://www.anthropic.com
  tags:
  - Anthropic
  - Best Practices
  - Engineering
  properties:
  - type: Documentation
    url: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
  - type: Reference
    url: https://docs.anthropic.com/en/docs/agents-and-tools/agent-best-practices
  x-features:
  - Frames context as a finite attention budget
  - Distinguishes context engineering from prompt engineering
  - Covers system prompts, tools, few-shot, and retrieval
  - Long-horizon strategies (compaction, notes, sub-agents)
  x-useCases:
  - Building production AI agents and assistants
  - Tuning system prompts and toolsets for reliability
  - Designing memory and compaction for long-running agents
- aid: context-engineering:retrieval-augmented-generation
  name: Retrieval-Augmented Generation (RAG)
  description: RAG is a context engineering pattern that augments LLM prompts with passages retrieved at inference time from
    a vector store, search index, or knowledge base. RAG keeps facts outside the model and is one of the most widely used
    context engineering techniques.
  humanURL: https://arxiv.org/abs/2005.11401
  baseURL: https://arxiv.org
  tags:
  - Embeddings
  - Knowledge Base
  - RAG
  - Retrieval
  properties:
  - type: Specification
    url: https://arxiv.org/abs/2005.11401
  - type: Reference
    url: https://docs.llamaindex.ai/
  - type: Reference
    url: https://python.langchain.com/docs/concepts/rag/
  x-features:
  - Pluggable retrievers over vector and keyword indexes
  - Pre-retrieval rewriting and post-retrieval re-ranking
  - Hybrid retrieval combining BM25 and dense vectors
  - Citations and grounding for answer auditing
  x-useCases:
  - Domain-specific question answering over private documents
  - Customer support agents with up-to-date knowledge
  - Long-tail factual recall outside model training
- aid: context-engineering:prompt-engineering
  name: Prompt Engineering
  description: Prompt engineering is the discipline of crafting model instructions and examples to guide model behavior. Prompt
    engineering remains a sub-discipline of context engineering and includes techniques like role prompting, chain-of-thought,
    few-shot examples, and structured output formats.
  humanURL: https://www.promptingguide.ai/
  baseURL: https://www.promptingguide.ai
  tags:
  - Few-Shot
  - Instructions
  - Prompting
  properties:
  - type: Documentation
    url: https://www.promptingguide.ai/
  - type: Reference
    url: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
  - type: Reference
    url: https://platform.openai.com/docs/guides/prompt-engineering
  x-features:
  - Chain-of-thought and reasoning prompts
  - Few-shot, zero-shot, and self-consistency prompts
  - Structured output formatting (JSON, XML)
  - Role and persona instructions
  x-useCases:
  - Steering model behavior in zero-shot tasks
  - Eliciting structured responses suitable for downstream tools
  - Mitigating undesired outputs through guardrails
- aid: context-engineering:agent-loops
  name: Agentic Loops and Tool Use
  description: 'Agentic loops are iterative reasoning patterns in which an LLM plans, calls tools, observes results, and refines
    its plan. Tool design is a central context engineering concern: tools must be token-efficient, have minimal overlap, and
    include clear, motivating descriptions.'
  humanURL: https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview
  baseURL: https://docs.anthropic.com
  tags:
  - Agents
  - Function Calling
  - ReAct
  - Tool Use
  properties:
  - type: Documentation
    url: https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview
  - type: Reference
    url: https://platform.openai.com/docs/guides/function-calling
  - type: Reference
    url: https://arxiv.org/abs/2210.03629
  x-features:
  - Tool definitions with JSON Schema arguments
  - Iterative plan-act-observe loops
  - Parallel tool invocation
  - Server- and client-side tool execution
  x-useCases:
  - Building task-completing AI agents
  - Wiring LLMs to internal APIs and databases
  - Decomposing complex problems with sub-tools
- aid: context-engineering:long-horizon-strategies
  name: Long-Horizon Context Strategies
  description: Long-horizon strategies handle conversations and tasks that exceed the context window. Techniques include compaction
    (summarizing history into a smaller representation), structured note taking (persistent external memory), and multi-agent
    decomposition where sub-agents handle bounded subtasks and return condensed summaries.
  humanURL: https://www.anthropic.com/news/contextual-retrieval
  baseURL: https://www.anthropic.com
  tags:
  - Compaction
  - Long Context
  - Memory
  - Multi-Agent
  properties:
  - type: Documentation
    url: https://www.anthropic.com/news/contextual-retrieval
  - type: Reference
    url: https://www.anthropic.com/research/swe-bench-sonnet
  - type: Reference
    url: https://github.com/microsoft/autogen
  x-features:
  - Conversation summarization for compaction
  - Persistent memory files for cross-session knowledge
  - Multi-agent decomposition with bounded sub-agents
  - Hierarchical planning over long-running tasks
  x-useCases:
  - Long-running coding agents and SWE assistants
  - Multi-day customer engagements requiring memory
  - Complex research tasks decomposed across sub-agents
common:
- type: Reference
  url: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
- type: Reference
  url: https://www.promptingguide.ai/
- type: Reference
  url: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
- type: Reference
  url: https://docs.llamaindex.ai/
- type: Reference
  url: https://python.langchain.com/docs/concepts/rag/
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com