From Prompt Engineering to Context Engineering

A few years ago, many predicted that prompt engineering would quickly become obsolete. Instead, it evolved and grew more essential, giving rise to context engineering—a broader, richer discipline dedicated to optimizing not just prompts, but the full context provided to Large Language Models (LLMs) and multimodal AI systems.

Prompt engineering isn’t merely asking questions—it’s carefully designing, structuring, and refining instructions to shape how AI responds. Context engineering expands on this by systematically architecting all relevant context, including instructions, inputs, tools, memory, and historical data, to ensure the model achieves optimal results.

What Exactly Is Context Engineering?

Context engineering involves the iterative process of crafting, optimizing, and testing the inputs provided to AI systems, ensuring they have precisely the information they need, structured in the right way, to perform their tasks effectively. This includes:

Structuring inputs and outputs (e.g., JSON schemas)
Managing dynamic elements (user inputs, dates, etc.)
Utilizing Retrieval-Augmented Generation (RAG) and memory stores
Tool integration and structured output parsing
Handling historical context and application state

Essentially, you’re not just writing prompts; you’re designing a thoughtful and comprehensive context environment for your AI models.

Context Engineering in Action

Let’s see what this looks like through a practical example of an AI-powered research workflow I developed recently.

A Concrete Example: Building a Research Planner Agent

I built an AI agent that takes complex user queries and breaks them down into targeted subtasks to optimize web searches. The workflow requires precise context to guide the agent in generating structured subtasks effectively.

Example of a System Prompt:

You are an expert research planner. Your task is to break down a complex research query (delimited by <user_query></user_query>) into specific search subtasks, each focusing on a different aspect or source type.

The current date and time is: {{ $now.toISO() }}

For each subtask, provide:
1. A unique string ID (e.g., 'subtask_1')
2. A specific search query
3. Source type (web, news, academic, specialized)
4. Time period relevance (today, recent, past_year, etc.)
5. Domain focus (technology, science, health, etc.)
6. Priority level (1-highest to 5-lowest)

Create 2 subtasks covering different aspects of the topic.

After creating subtasks, infer and include `start_date` and `end_date` based on the current date and time_period provided.

Structured Inputs and Outputs

To ensure the model returns consistent and usable outputs, I explicitly defined the expected JSON structure:

{
  "subtasks": [
    {
      "id": "openai_latest_news",
      "query": "latest OpenAI announcements and news",
      "source_type": "news",
      "time_period": "recent",
      "domain_focus": "technology",
      "priority": 1,
      "start_date": "2025-06-03T06:00:00.000Z",
      "end_date": "2025-06-11T05:59:59.999Z"
    },
    ...
  ]
}

Providing examples like this guides the AI clearly and reduces the risk of unpredictable or inconsistent outputs. But this is not the ultimate solution to extracting structured outputs from LLMs. A better Alternative is discussed in the next section.

Structured Inputs and Outputs (feat. Instructor)

One of the biggest pain points in working with LLMs is getting consistent, structured outputs—especially when you’re chaining tools or feeding data downstream. This is where I found the Instructor library to be a game-changer.

Instead of writing verbose prompts to “guide” the model into outputting a specific schema, Instructor lets you define a Pydantic or dataclass model, and it takes care of enforcing structure from almost any generative model. It’s clean, type-safe, and surprisingly reliable.

Here’s an example of how I used it to define the subtask schema for my planning agent:

from pydantic import BaseModel
from typing import Optional

class Subtask(BaseModel):
    id: str
    query: str
    source_type: str  # "news", "web", etc.
    time_period: Optional[str] = None  # "today", "recent", etc.
    domain_focus: Optional[str] = None
    priority: int
    start_date: str
    end_date: str

Then, you can call the model like this:

from instructor import Instructor
from openai import OpenAI

client = Instructor(OpenAI())

subtasks = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Break down the following query into two subtasks: What's the latest dev news from OpenAI?"}],
    response_model=List[Subtask]
)

The result? Clean, valid JSON outputs—typed, validated, and ready to plug into your workflows.

I’ve used Instructor where I needed predictability across components. It really shines when you need:

Reliable field formatting
Fail-fast error handling
Seamless integration with Python-based workflows
It retries request to the LLM automatically in case of failed validation (in rare cases)

Even if you’re not deep into Python, understanding this approach helps clarify the goal of context engineering: giving the model not only guidance but also constraints.

Tool Integration: Date and Time Context

Including dynamic elements, like date and time, dramatically improves AI responses. Rather than letting the model guess, providing accurate date/time context ensures the subtasks generated are relevant and timely.

Example integration in n8n:

The current date and time is: {{ $now.toISO() }}
This small piece of context significantly improves the quality and accuracy of results, especially for time-sensitive tasks.

Memory and Retrieval-Augmented Generation (RAG)

Context engineering extends to intelligently managing memory. For example, caching previously generated subtasks using a vector store helps quickly retrieve past results, making your AI workflow faster and less costly.

Why Context Engineering Matters

Good context engineering provides clear, structured, and optimized inputs that enable LLMs to generate consistently better outputs. It reduces the ambiguity of prompts, significantly improves accuracy, optimizes resource use, and facilitates more reliable workflows.

It’s not about writing longer prompts—it’s about providing smarter context.

Advanced Topics and Future Directions

Context engineering continues to evolve rapidly, encompassing topics like context compression, context safety, and automated context optimization techniques. Expect it to become an increasingly critical skill set for AI developers as AI applications grow more complex and nuanced.

Context Engineering: The Next Evolution in Prompt Design