Context engineering is the practice of designing systems that determine what information an AI model sees before it generates a response to user input. It goes beyond formatting prompts or crafting instructions, instead shaping the entire environment the model operates in: grounding data, schemas, tools, constraints, policies, and the mechanisms that decide which pieces of information make it into the model’s input at any moment. In applied terms, good context engineering means establishing a small set of high-signal tokens that improve the likelihood of a high-quality outcome.

Think of prompt engineering as a predecessor discipline to context engineering. While prompt engineering focuses on wording, sequencing, and surface-level instructions, context engineering extends the discipline into architecture and orchestration. It treats the prompt as just one layer in a larger system that selects, structures, and delivers the right information in the right format so that an LLM can plausibly accomplish its assigned task.

What does ‘context’ mean in AI?

In AI systems, context refers to everything an a large language model (LLM) has access to when producing a response — not just the user’s latest query, but the full envelope of information, rules, memory, and tools that shape how the model interprets that query. The total amount of information the system can process at once is called the context window. The context consists of a number of different layers that work together to guide model behavior:

• The system prompt defines the model’s role, boundaries, and behavior. This layer can include rules, examples, guardrails, and style requirements that persist across turns.
• A user prompt is the immediate request — the short-lived, task-specific input that tells the model what to do right now.
• State or conversation history acts as short-term memory, giving the model continuity across turns by including prior dialog, reasoning steps, and decisions.
• Long-term memory is persistent and spans many sessions. It contains durable preferences, stable facts, project summaries, or information the system is designed to reintroduce later.
• Retrieved information provides the model with external, up-to-date knowledge by pulling relevant snippets from documents, databases, or APIs. Retrieval-augmented generation turns this into a dynamic and domain-specific knowledge layer.
• Available tools consist of the actions an LLM is capable of performing with the help of tool calling or MCP servers: function calls, API endpoints, and system commands with defined inputs and outputs. These tools help the model take actions rather than only produce text.
• Structured output definitions that tell the model exactly how its response should be formatted — for example, requiring a JSON object, a table, or a specific schema.

Together, these layers form the full context an AI system uses to generate responses that are hopefully accurate and grounded. However, a host of difficulties with context in AI can lead to suboptimal results.

What is context failure?

The term “context failure” describes a set of common breakdown modes when AI context systems go wrong. These failures fall into four main categories:

• Context poisoning happens when a hallucination or other factual error slips into the context and then gets used as if it were truth. Over time, the model builds on that flawed premise, compounding mistakes and derailing reasoning.
• Context distraction occurs when the context becomes too large or verbose. Instead of reasoning from training data, the model can overly focus on the accumulated history — repeating past actions or clinging to old information rather than synthesizing a fresh, relevant answer.
• Context confusion arises when irrelevant material — extra tools, noisy data, or unrelated content — creeps into context. The model may treat that irrelevant information as important, leading to poor outputs or incorrect tool calls.
• Context clash occurs when new context conflicts with earlier context. If information is added incrementally, earlier assumptions or partial answers may contradict later, clearer data — resulting in inconsistent or broken model behavior.

One of the advances that AI players like OpenAI and Anthropic have offered for their chatbots are the capability to handle increasingly large context windows. But size isn’t everything, and indeed larger windows can be more prone to the sorts of failures described here.  Without deliberate context management — validation, summarization, selective retrieval, pruning, or isolation — even large context windows can produce unreliable or incoherent outcomes.

What are some context engineering techniques and strategies?

Context engineering aims to overcome these types of context failures.  Here are some of the main techniques and strategies to apply:

• Knowledge base or tool selection. Choose external data sources, databases, documents or tools the system should draw from. A well-curated knowledge base directs retrieval toward relevant content and reduces noise.
• Context ordering or compression. Decide which pieces of information deserve space and which should be shortened or removed. Systems often accumulate far more text than the model needs, so pruning or restructuring keeps the high-signal material while dropping noise. For instance, you could replace a 2,000-word conversation history with a 150-word summary that preserves decisions, constraints, and key facts but omits chit-chat and digressions. Or you could sort retrieved documents by relevance score and inject only the top two chunks instead of all twenty. Both approaches keep the context window focused on the information most likely to produce a correct response.
• Long-term memory storage and retrieval design. Defines how persistent information — including user preferences, project summaries, domain facts, or outcomes from prior sessions — is saved and reintroduced when needed. A system might store a user’s preferred writing style once and automatically reinsert a short summary of that preference into future prompts, instead of requiring the user to restate it manually each time. Or it could store the results of a multi-step research task so the model can recall them in later sessions without rerunning the entire workflow.
• Structured information and output schemas. These allow you to provide predictable formats for both context and responses. Giving the model structured context — such as a list of fields the user must fill out or a predefined data schema — reduces ambiguity and keeps the model from improvising formats. Requiring structured output does the same: for instance, demanding that every answer conform to a specific JSON shape lets downstream systems validate and consume the output reliably.
• Workflow engineering. You can link multiple LLM calls, retrieval steps, and tool actions into a coherent process. Rather than issuing one giant prompt, you design a sequence: gather requirements, retrieve documents, summarize them, call a function, evaluate the result, and only then generate the final output. Each step injects just the right context at the right moment. A practical example is a customer-support bot that first retrieves account data, then asks the LLM to classify the user’s issue, then calls an internal API, and only then composes the final message.
• Selective retrieval and retrieval-augmented generation. This technique applies filtering so the model sees only the parts of external data that matter. Instead of feeding the model an entire knowledge base, you retrieve only the paragraphs that match the user’s query. One common example is chunking documents into small sections, ranking them by semantic relevance, and injecting only the top few into the prompt. This keeps the context window small while grounding the answer in accurate information.

Together, these approaches allow context engineering to deliver a tighter, more relevant, and more reliable context window for the model — minimizing noise, reducing the risk of hallucination or confusion, and giving the model the right tools and data to behave predictably.

Why is context engineering important for AI agents?

Context engineering gives AI agents the information structure they need to operate reliably across multiple steps and decisions. Strong context design treats the prompt, the memory, the retrieved data, and the available tools as a coherent environment that drives consistent behavior. Agents depend on this environment because context is a critical but limited resource for long-horizon tasks.

Agents fail most often when their context becomes polluted, overloaded, or irrelevant. Small errors in early turns can accumulate into large failures when the surrounding context contains hallucinations or extraneous details. Good context engineering improves their efficiency by giving them only the information they need while filtering out noise. Techniques like ranked retrieval and selective memory keep the context window focused, reducing unnecessary token load and improving responsiveness.

Context also enables statefulness — that is, the ability for agents to remember preferences, past actions, or project summaries across sessions. Without this scaffolding, agents behave like one-off chatbots rather than systems capable of long-term adaptation.

Finally, context engineering is what allows agents to integrate tools, call functions, and orchestrate multi-step workflows. Tool specifications, output schemas, and retrieved data all live in the context, so the quality of that context determines whether the agent can act accurately in the real world. In tool-integrated agent patterns, the context is the operating environment where agents reason and take action.

Context engineering guides

Want to learn more? Dive deeper into these resources:

• LlamaIndex’s “What is context engineering — what it is and techniques to consider“: A solid foundational guide explaining how context engineering expands on prompt engineering, and breaking down the different types of context that need to be managed.
• Anthropic’s “Effective context engineering for AI agents”: Explains why context is a finite but critical resource for agents, and frames context engineering as an essential design discipline for robust LLM applications.
• SingleStore’s “Context engineering: A definitive guide”: Walks you through full-stack context engineering: how to build context-aware, reliable, production-ready AI systems by integrating data, tools, memory, and workflows.
• PromptingGuide.ai’s “Context engineering guide”: Offers a broader definition of context engineering (across LLM types, including multimodal), and discusses iterative processes to optimize instructions and context for better model performance.
• DataCamp’s “Context engineering: A guide with examples“: Useful primer that explains different kinds of context (memory, retrieval, tools, structured output), helping practitioners recognize where context failures occur and how to avoid them.
• Akira.ai’s “Context engineering: Complete guide to building smarter AI systems“: Emphasizes context engineering’s role across use cases from chatbots to enterprise agents, and stresses the differences with prompt engineering for scalable AI systems.
• Latitude’s “Complete guide to context engineering for coding agents”: Focuses specifically on coding agents and how context engineering helps them handle real-world software development tasks accurately and consistently

These guides form a strong starting point if you want to deepen your understanding of context engineering — what it is, why it matters, and how to build context-aware AI systems in practice. As models grow more capable, mastering context engineering will increasingly separate toy experiments from reliable, production-grade agents.