What is context engineering? The complete guide

In July 2025, Gartner declared that context engineering is in and prompt engineering is out. It predicted context engineering will appear in 80% of AI tools by 2028.

By mid-2026, that shift had already happened.

Context engineering is now the discipline that separates AI systems that work reliably in production from AI experiments that impress in demos and fail at scale.

Key takeaways

Context engineering designs the entire information environment an AI model operates in, not just the prompt.
Gartner predicts it will appear in 80% of AI tools by 2028. It is already the defining technical discipline of agentic AI.
Prompt engineering is a subset of context engineering. A well-crafted prompt inside a poorly engineered context still produces unreliable results.
Four components define the discipline: Memory, retrieval, tools, and state. Each determines what the model knows and can act on at inference time.
Most AI agent failures are context failures, not model failures. The agent had the wrong information, the wrong tools, or lost track of its state.
For businesses, context engineering is an infrastructure problem. Deploying AI reliably across a team requires governing what context AI systems can access, from where, and in what form.

What is context engineering?

Context engineering is the discipline of designing the entire information environment that an AI model operates in.

Not just the prompt. The prompt is one element. Context engineering is everything surrounding it.

Toby Lutke, CEO of Shopify, described it as “the art of providing all the context necessary for the task to be done to the LLM.” Gartner’s formulation: context engineering gives AI systems “the situational awareness needed to act with relevance and precision. Without it, you have a model that can reason but cannot see.”

What context engineering actually includes:

The documents, records, and knowledge the model can reference
The conversation and session history it remembers
The tools and external systems it can call
The permissions and scope it operates within
The current state of the task and what has already been done
The format and structure in which all of the above is presented

A practical illustration:

Scenario: you ask an AI agent to “write a quarterly business review for Q1.”

Approach	What the agent sees	What it produces
Prompt engineering only	A generic instruction	A generic template with placeholder data
Context engineering	Q1 revenue data, prior QBR format, strategic priorities, milestone status	A draft that reads like someone who works at your company wrote it

The difference is not the prompt. It is the context.

How context engineering differs from prompt engineering

Prompt engineering is the practice of crafting the instruction you give a model to improve its output. It is about word choice, instruction structure, role assignment, and chain-of-thought formatting.

Context engineering is the practice of designing the entire information system the model operates in.

Dimension	Prompt engineering	Context engineering
What it addresses	How the instruction is phrased	What the model sees before, during, and after the instruction
Primary concern	Instruction clarity	Information architecture
Scope	The prompt itself	Memory, retrieval, tools, state, conversation history
Failure mode	The model misunderstands the task	The model understands the task but lacks the information to do it
Role today	A useful technique	The foundational discipline of production AI
Who practices it	Any AI user	AI engineers, agent architects, enterprise data teams

The key distinction: prompt engineering is a subset of context engineering. A perfectly written prompt injected into a context that lacks the right information, the right tools, and the right state management will still produce unreliable results.

For the practical skill of writing effective prompts as one input into the broader context, see prompt engineering for business teams.

The four components of context engineering

A model’s behavior at inference time is determined by four layers. Context engineering designs all of them.

1. Memory

Memory is the information that persists within and across AI sessions.

Two types matter:

Short-term memory: the context window itself. Everything the model can see within a single inference call.
Long-term memory: information stored externally and retrieved when relevant. User preferences, past interactions, organizational knowledge.

The core problem context engineering solves here: context windows are finite. Adding everything to short-term memory causes “context rot” where the model’s performance degrades as the window fills with irrelevant information.

Context engineering determines what to keep, what to compress, and what to offload to long-term storage.

For a deeper look at this challenge, see what is context rot and AI agent memory and context.

2. Retrieval

Retrieval is the mechanism by which relevant information is fetched from external systems and injected into the model’s context at the right moment.

Retrieval-Augmented Generation (RAG) is the most common retrieval pattern:

Documents are indexed as vector embeddings
At query time, semantically similar chunks are retrieved
Retrieved content is added to the context before the model responds

The context engineering problem with retrieval:

Too much irrelevant information degrades performance
Too little leaves the model without what it needs

Context engineering designs the full retrieval pipeline: what is indexed, how it is chunked, what is retrieved, and how retrieved content is presented to the model.

For enterprise RAG implementation, see retrieval augmented generation for enterprise.

3. Tools

Tools are the external capabilities the model can call: web search, code execution, database queries, API calls, file access, calendar integrations, CRM lookups.

In agentic AI systems, the model does not just generate text. It decides which tools to use, calls them, receives results, and incorporates those results into its reasoning.

Tool-related context engineering decisions:

Which tools are available to the model for each task
How those tools are described (poor descriptions lead to misuse or avoidance)
What permissions each tool has
How tool outputs are formatted and presented back to the model

A model given access to too many tools spends cognitive capacity choosing between them. A model given poorly described tools will misuse them or ignore them.

For the technical standard that connects agents to tools at scale, see Claude Code MCP vs tool use.

4. State

State is the information about the current situation: where the user is in a workflow, what has already been done, what decisions have been made, what constraints apply.

State complexity scales with task complexity:

Simple chatbot: state is just the conversation history
Multi-step agentic workflow: the agent must track what actions it has taken, what the results were, where it is in a plan, and what it still needs to do

Agents that lose track of state make redundant actions, repeat steps, and fail to complete multi-step tasks reliably. State management is one of the primary differentiators between agents that work in demos and agents that work in production.

The four context operations

Beyond the four components, context engineering involves four operations that determine how context is managed dynamically:

Operation	What it does
Context writing	Adding information to the context: system prompt, retrieved documents, tool outputs, conversation history
Context selection	Deciding what information to include for a specific query. Not all available information is relevant to every task.
Context compression	Summarizing or reducing old context to make room for new information without losing essential content
Context offloading	Moving information out of the active context window into external storage, retrieved on demand

Why context engineering is the most important AI skill right now

The shift from prompt engineering to context engineering reflects a shift in where AI value is created.

The bottleneck has moved:

2023 to 2024: Model capability was the primary bottleneck. Getting a model to reason well required careful prompt construction.
2025 to present: Models became capable enough that reasoning quality is no longer the primary bottleneck for most applications. The bottleneck shifted to information.

The question is no longer “can the model reason about this?” It is “does the model have the right context to act correctly?”

Three reasons context engineering is the skill that matters most:

Reliability at scale. A single well-crafted prompt can produce a great output once. Context engineering is what makes an AI system produce reliable outputs consistently, across thousands of users, on tasks it has never seen before.
Agent performance. Agent failures in production are overwhelmingly context failures. The agent received the wrong tools. It lost track of its state. It retrieved irrelevant documents. Fixing these failures requires context engineering, not prompt rewriting.
Production vs. demo gap. Demo-quality AI is a well-prompted model. Production AI is a well-engineered context system around a capable model.

Context engineering in enterprise AI

For enterprises, context engineering is primarily an infrastructure and governance problem, not a prompting problem.

What enterprise AI systems need in their context:

Proprietary company data (financial records, customer records, product information)
Operational knowledge (SOPs, decision frameworks, process documentation)
Metric definitions and business logic (how your company defines revenue, customer, churn)
Organizational context (team structures, reporting relationships, strategic priorities)
Real-time data (current pipeline status, recent customer interactions, live inventory)

None of this comes pre-loaded into a model. It must be structured, governed, and made available through context infrastructure: knowledge bases, retrieval systems, tool permissions, and state management.

For a look at how to structure this knowledge for AI consumption, see how to structure AI-friendly knowledge base and what is an AI context pack.

The governance problem:

The harder enterprise challenge is not building the retrieval system. It is governing what context AI systems can access.

An AI agent that can query the CRM needs controlled access to customer data
An agent that can access financial records needs appropriate permission boundaries
An agent operating on behalf of different roles should see different context depending on who is using it

Context engineering at enterprise scale is not just a technical problem. It is a data governance problem, an access management problem, and an organizational knowledge management problem.

Context engineering vs. RAG

RAG (Retrieval-Augmented Generation) is a specific pattern within context engineering. It is not the whole discipline.

	RAG	Context engineering
What it covers	The retrieval component: indexing docs, retrieving chunks, injecting them	The full architecture: memory, retrieval, tools, state, and how they interact
Problem it solves	Giving the model access to external knowledge it was not trained on	Designing the entire information environment the model operates in
Relationship	A technique within context engineering	The broader discipline that includes RAG and much more

A well-implemented RAG pipeline is good context engineering. A well-implemented RAG pipeline paired with poor memory management, poor tool design, and poor state management is incomplete context engineering.

What context engineering means for business leaders

You do not need to build context systems to understand why they matter for your AI investments.

Three practical implications:

1. The quality of your AI outputs is determined by your context infrastructure, not the model you chose.

Organizations that invest in making their knowledge accessible, organized, and machine-readable get dramatically better AI results than organizations that deploy the same model with no context infrastructure.

2. “We have ChatGPT” is not a context engineering strategy.

A consumer AI tool with no access to your company’s data, processes, and knowledge produces generic outputs. The gap between generic AI outputs and genuinely useful AI outputs is almost entirely an information architecture gap.

3. Governing AI context is as important as governing AI systems.

What your AI systems can see determines what they can do. An AI system with access to customer PII, financial records, and strategic plans creates different risk than one operating within governed data boundaries. Context governance is a necessary part of AI governance.

Need help building your AI context infrastructure?

Most organizations deploy AI tools before they have the context infrastructure to make those tools genuinely useful. The result is AI that produces mediocre outputs, loses trust with the team, and gets abandoned.

Phos AI Labs is an embedded AI consulting firm for mid-market businesses.

We identify the right AI problems, build the strategy, handle implementation, and train your team until AI is how the business actually runs.

Strategy before systems: We establish which context infrastructure your specific AI use cases require before any platform selection begins.
AI Foundations that hold: We install the operating context, decision rules, and configuration standards your team runs on for years.
Real team training: We build AI fluency inside your actual workflows, not in generic sessions.
Private AI Workspace: We design a company-wide AI environment with your knowledge base, access controls, and context governance built in from the start.
AI-Native Operations design: We rebuild the workflows that matter most so AI compounds across your business.
Honest judgment, every time: We tell you which context investments your situation actually requires and which are unnecessary overhead.
We stay until it compounds: We are not done when the system is built. We are done when the team runs it reliably.

400+ engagements. Clients include Zapier, Coca-Cola, Medtronic, Sotheby’s, Dataiku, and American Express.

If you want AI that actually knows your business, talk to the team at Phos AI Labs.

FAQs

What is context engineering in simple terms?

Context engineering is the practice of designing everything an AI model sees before generating a response: the documents it can reference, the tools it can use, and the task state.

What is the difference between context engineering and prompt engineering?

Prompt engineering is about how you phrase the instruction you give a model. Context engineering is about what the model knows and can access.

A good prompt in a bad context produces bad results.

Why is context engineering important?

AI agent failures are overwhelmingly context failures: the agent had the wrong information, the wrong tools, or lost track of state.

Gartner predicts context engineering will appear in 80% of AI tools by 2028.

What are the four components of context engineering?

Memory (what the model retains), retrieval (how relevant information is fetched from external sources), tools (what external capabilities the model can call), and state (what the model knows about the task).

What is RAG, and how does it relate to context engineering?

RAG is a retrieval pattern where documents are indexed and relevant chunks are retrieved and injected into the model’s context.

RAG is one technique within context engineering. The broader discipline covers memory, tools, and state.

What does context engineering mean for businesses?

For businesses, the quality of AI outputs depends primarily on the quality of your context infrastructure, not the model you chose. Organizing your knowledge and governing data access determines whether AI produces genuinely useful outputs.