Context Engineering Explained
- Priank Ravichandar
- Feb 3, 2025
- 3 min read
Updated: Oct 23, 2025
An overview of the context engineering for product team members and stakeholders.
Key Takeaways
We use context engineering to curate the context AI model’s use to generate outputs.
Context engineering is crucial because there are limits to how much AI can “remember.”
Context quality can degrade over time as conversations grow longer.
While prompt engineering focuses on how to write effective prompts, context engineering focuses on curating what context is provided at each request.
Overview
Note: Generated using NotebookLM using content from Anthropic | Effective context engineering for AI agents, Datacamp | Context Engineering: A Guide With Examples, and Elevate With Addy Osmani | Context Engineering: Bringing Engineering Discipline to Prompts.
FAQs
What Is Context Engineering?
We use context engineering to curate the context AI model’s use to generate outputs.
Context engineering refers to the set of strategies for curating the context provided to AI models. We provide context to the model with every interaction. When responding to new queries, it must recall all the relevant context from current and previous interactions. Without context engineering, the model must decide what details are relevant (from all the context available). If it fails to identify the right context, it will produce bad outputs. However, with context engineering, we can design systems that dynamically decide what context the model sees before it generates a response. This ensures that it successfully completes tasks.
Why is context engineering important?
Context engineering is crucial because there are limits to how much AI can “remember.”
AI models can handle massive amounts of data. However, these models have a finite context window (or “context length”), which is the amount of information that they can “remember” at any one time. When presented with a lot of information, they can struggle to recall the right details when completing tasks, just like humans. Over time, as the amount of potentially relevant content grows, it becomes harder to keep track of what’s relevant and what’s not. Therefore, we must manage the amount of context provided with each request. If they don’t have the right context, they won’t produce the right output.
Why is context engineering especially important for AI agents?
Context engineering is critical for AI agents, which often engage in interactions spanning hundreds of turns. When performing long-running tasks, they continuously accumulate feedback from calling tools and interacting with systems. This can cause the available context to rapidly expand, making it harder for the agent to think, plan, and act.
What problems does AI encounter with longer context?
Context quality can degrade over time as conversations grow longer, causing several problems:
Context poisoning occurs when a hallucination or error enters the context and is repeatedly referenced in future responses.
Context distraction occurs when context grows so large that the model loses focus and starts making mistakes long before the context window is actually full.
Context confusion occurs when irrelevant context influences the model's response.
Context clash occurs when conflicting information or early, incorrect attempts to answer a question remain in the context, degrading subsequent reasoning and performance.
What does “good” context look like?
Good context includes sufficient information to fully outline the model’s expected behavior.
Context engineering focuses on finding the smallest amount of information that maximizes the likelihood of the desired outcome. Minimal does not necessarily mean short. Good context has several attributes, such as:
It includes structured prompts, specific instructions, and examples.
It includes relevant information (e.g., code snippets, examples, documents, specs, and logs).
It clarifies tool use so the model can understand when, where, and how to use them.
It highlights relevant details from past conversations/requests.
Like good software architecture, good context engineering requires intentional information management - deciding not just what to include, but when to exclude, summarize, or refresh. — Addy Osmani, Engineering Leader at Google
How is context engineering different from prompt engineering?
Most AI applications require both prompt engineering and context engineering.
Prompt engineering focuses on how to write effective prompts. It guides the model’s behavior by providing good instructions and initial context. It refers to methods for writing and organizing LLM instructions for optimal outcomes. It is a discrete task that ends once a prompt has been crafted.
Context engineering focuses on curating what context is provided at each request. It guides the model’s behavior by managing context flow across multiple interactions. It is an iterative task that must happen at each interaction with the model to direct its behavior effectively. Unlike a single hard-coded prompt, it is dynamic and situation-specific.
