Unlocking AI Potential: A Guide to Context Engineering

To save you hours of scrolling through X and Reddit, this article breaks down the essentials of context engineering. The outline of this article will first explain what context engineering is, why it came about, and its relevance. Then, we'll cover how to perform context engineering, complete with some concrete examples.

What is Context Engineering?

Context engineering is the practice of designing and building dynamic systems that provide a Large Language Model (LLM) with the right information, in the right format, at the right time to accomplish a specific task. In other words, it's about perfectly packing the context window—the input area of an LLM.

Context Engineering vs. Prompt Engineering

A key point is that context engineering is primarily relevant for those building LLM applications, including AI agents. While you may have read that context engineering is the new prompt engineering, this doesn't render prompt engineering obsolete.

For instance, if you're simply chatting with a chatbot like ChatGPT about choosing running shoes—discussing cushioning, price ranges, and matching outfits—that interaction is all about prompting. Prompt engineering remains entirely relevant in those conversational scenarios.

Context engineering becomes crucial when you're building AI applications like agents. Unlike a simple back-and-forth chat, an AI agent cannot be iteratively guided to the correct answer. It requires a comprehensive set of instructions that covers all potential actions and scenarios it might encounter.

Consider a customer service AI agent for an online store. This agent must be equipped to handle a multitude of inquiries: - Billing problems - Refund issues - Login difficulties - Searching terms and conditions - Answering irrelevant questions - Handling user frustration or abuse - Escalating to a human when necessary

To give the agent all the instructions and resources needed for these tasks, the prompts become larger and more complex, often resembling code with XML tags and markdown. This complexity is fundamentally different from a simple conversation with ChatGPT, which is why the term "context engineering" emerged.

Context engineering isn't a brand-new technique but rather a natural progression of prompt engineering tailored for the specific needs of crafting complex prompts for AI applications. As Andrej Karpathy explains, the LLM is the CPU, and the context window is the RAM.

The Core Components of an AI Agent

To understand what context engineering looks like in practice, let's examine the structure of an AI agent. An AI agent is a software system that uses AI to pursue goals and complete tasks on behalf of users. Examples include customer service agents, sales assistant agents, and coding agents.

Regardless of their specific function, most AI agents are built from several essential components:

  1. Model: Every agent needs an AI model, whether it's from OpenAI, Anthropic, Google, or an open-source alternative. The choice depends on the specific requirements of the task.
  2. Tools: Tools enable agents to interact with external systems. For example, a personal assistant agent needs a tool to access a Google Calendar to book appointments.
  3. Knowledge and Memory: Most agents require a way to store and retrieve information. A therapy AI agent needs memory to recall previous conversations, while a legal AI agent needs a knowledge base of specific case files.
  4. Audio and Speech: Equipping an agent with audio and speech capabilities allows for more natural and intuitive interactions.
  5. Guardrails: These are safety mechanisms to ensure the agent behaves appropriately. You wouldn't want a customer service agent swearing at users.
  6. Orchestration: These systems are for deploying, monitoring, and improving the agent over time. An agent shouldn't be released into the wild without ongoing oversight.

A useful analogy is to think of these components like the parts of a burger. A burger needs a bun, a patty, vegetables, and condiments to be a burger. You can vary the types of each—a whole wheat bun, a veggie patty—but the core components must be present. Similarly, an AI agent needs these building blocks to function.

Continuing the analogy, if you were an alien unfamiliar with burgers, you would need an instruction manual explaining how to assemble them. The context engineer provides this "instruction manual" for the AI agent. The engineered prompt details exactly how all the components work together: what the tools are, how to use them, how to access memory, what's in the knowledge base, and when to use speech functionalities.

This resulting prompt is the master instruction set for the agent. Getting this prompt right is critical, and engineers spend significant time on context engineering to perfect it.

A Practical Example: AI Research Assistant Prompt

Here is a full example of a context-engineered prompt for an AI research assistant designed to keep up with AI trends. The general structure uses markdown headings (#) to structure the prompt for clarity.

# Role
You are an AI research assistant focused on identifying and summarizing recent trends in AI from multiple source types. Your job is to break down a user's query into actionable subtasks and return the most relevant insights based on engagement and authority.

# Task
Given a research query delimited by the XML tags <user_query> and </user_query>, do the following:
1. Extract up to 10 diverse, high-priority subtasks, each targeting a different angle or source type.
2. Prioritize by engagement (e.g., views, likes, reposts, citations) and source authority (e.g., publication reputation, domain expertise).
3. Generate a JSON output for each subtask in the format specified below.
4. Calculate the correct start_date and end_date in UTC ISO format based on the specified time period.
5. Summarize all findings into a single, concise trend summary (approx. 300 words max).

# Input
<user_query>
[Insert search query here]
</user_query>

# Output
You will output up to 10 subtasks in this exact JSON format:

{
  "id": "unique_id_for_subtask",
  "query": "specific_subquery_related_to_main_topic",
  "source_type": ["news", "X", "Reddit", "LinkedIn", "newsletter", "academic", "specialized"],
  "time_period_days": "integer from 1 to 10",
  "domain_focus": ["technology", "science", "health"],
  "priority": "integer from 1 (highest) to 10 (lowest)",
  "start_date": "YYYY-MM-DDTHH:MM:SSZ",
  "end_date": "YYYY-MM-DDTHH:MM:SSZ"
}

After performing all subtasks, write a final output summarizing the key recent trends.
- Limit the summary to around 300 words.
- Use bullet points or short paragraphs.
- Only include new, relevant, high-signal developments.
- Avoid fluff, background, or personal commentary.

# Constraints
- Focus on capturing the main point succinctly.
- Complete sentences and perfect grammar are not required.
- Ignore fluff, background information, and commentary.
- Do not include your own analysis or opinions.

# Capabilities & Reminders
- You have access to a web search tool to find and retrieve recent news articles.
- You must be deeply aware of the current date to ensure relevance, summarizing only information published within the past 10 days.

This is a relatively simple system prompt, yet it illustrates the complexity involved. In a more advanced setup, this could be split into a multi-agent system where one agent finds sources and another summarizes them. The implementation tool (e.g., n8n, OpenAI's Assistants API, LangChain) doesn't change the core principles of the prompt, which are applicable across different agentic systems.

Advanced Context Engineering Strategies

For those who want to dive deeper into context engineering and multi-agent systems, a couple of additional resources are highly recommended.

The first is a blog post from Cognition, which shares two fundamental principles for context engineering in multi-agent frameworks: 1. Always share context between your agents. 2. Recognize that actions carry implicit decisions, and these decision points must be handled carefully in the system's architecture and context.

The second article is from LangChain, showcasing a framework that breaks down common context engineering strategies: - Writing Context: Allowing an LLM to write down information about a task to save and use later. - Selecting Context: Pulling information from external sources to help an agent perform a task. - Compressing Context: Using techniques to condense large amounts of information into a more compact format for the LLM. - Isolating Context: Splitting context between different environments or stages.

Exploring these techniques is essential for building more sophisticated and effective AI applications.