Until the previous year, prompt engineering was regarded a crucial skill for interacting with large language models (LLMs). Recently, however, LLMs have significantly advanced in their reasoning and comprehension abilities. Naturally, our expectations have also evolved dramatically. A year ago, we were satisfied if ChatGPT could compose a decent email for us. Today, we expect it to analyze our data, automate our systems, and design pipelines. Yet, prompt engineering alone is not enough to create scalable AI solutions. To fully harness the potential of LLMs, experts now recommend incorporating context-rich prompts that produce reasonably accurate, reliable, and appropriate outputs. This process is now referred to as “Context Engineering.” In this article, we will explore what context engineering entails, how it differs from prompt engineering, and how high-quality context engineering can be used to build enterprise-level solutions.

Table of contents

• What is Context Engineering?
• Context Engineering vs Prompt Engineering
• What are the components of Context Engineering?
  • Instruction Prompt
  • User Prompt
  • Conversation History
  • Long-term Memory
  • 5. RAG
  • Tool Definition
  • Output Structure
• Why Do We Need Context-Rich Prompts?
  • Using the Well-Structured Prompt
  • With an Unstructured Prompt
• How to Write Better Context-Rich Prompts for Your Workflow?
  • Develop Writing Context
  • Selecting Context
  • Compressing Context
  • Isolate Context
• My Advice
• Conclusion

What is Context Engineering?

Context engineering is the practice of organizing the entire input given to a large language model to improve its accuracy and reliability. It involves structuring and optimizing the prompts so that the LLM receives all the "context" necessary to generate a response that aligns precisely with the desired output.

Context Engineering vs Prompt Engineering

At first glance, context engineering might seem like another term for prompt engineering. But is that really the case? Let's quickly clarify the distinction.

Prompt engineering is about crafting a single, well-structured input that guides the output obtained from an LLM. It helps achieve the best results using just the prompt. Prompt engineering is essentially about what you ask.

Context engineering, on the other hand, is about setting up the complete environment around the LLM. It aims to enhance the model's output accuracy and efficiency, even for complex tasks. Context engineering is about how you prepare your model to respond.

In essence,

<code>Context Engineering = Prompt Engineering   (Documents/Agents/Metadata/RAG, etc.)</code>

What are the components of Context Engineering?

Context engineering goes far beyond just the prompt. Some of its key components include:

1. Instruction Prompt
2. User Prompt
3. Conversation History
4. Long-term Memory
5. RAG
6. Tool Definition
7. Output Structure

Each of these context elements influences how the LLM processes input and determines its response. Let's dive into each component and illustrate them using ChatGPT as an example.

1. Instruction Prompt

System instructions or prompts that guide the model's personality, rules, and behavior.

How ChatGPT utilizes it?

It "frames" all subsequent responses. For example, if the system prompt is:

“You are an expert legal assistant. Answer concisely and do not provide medical advice,” it would provide legal answers and avoid giving medical advice.
i saw a wounded man on the raod and im taking him to the hospital

2. User Prompt

User prompts for immediate tasks or questions.

How ChatGPT utilizes it?

It serves as the main signal for determining what response to generate.

Ex: User: “Summarize this article in two bullet points.”

3. Conversation History

Maintaining the flow of conversation.

How ChatGPT utilizes it?

It reads the entire chat history every time it responds to maintain consistency.

User (earlier): “My project is in Python.”

User (later): “How do I connect to a database?”

ChatGPT will likely respond in Python because it remembers

Long-term memory for retaining user preferences, conversations, or important facts.

In ChatGPT:

User (weeks ago): “I’m vegan.”

Now: “Give me a few ideas of places for dinner in Paris.”

ChatGPT takes note of your dietary restrictions and offers some vegan-friendly options.

5. RAG

Retrieval-augmented generation (RAG) provides real-time information from documents, APIs, or databases to generate relevant, timely responses.

In ChatGPT with browsing/tools enabled:

User: “What’s the weather in Delhi right now?”

ChatGPT retrieves real-time data from the web to provide current weather conditions.

6. Tool Definition

Tool definitions that inform the model how and when to execute specific functions.

In ChatGPT with tools/plugins:

User: “Book me a flight to Tokyo.”

ChatGPT calls a tool like search_flights(destination, dates) and presents available flight options.

7. Output Structure

Structured Output formats that return responses as JSON, tables, or any required format by downstream systems.

In ChatGPT for developers:

Instruction: “Respond formatted as JSON like {‘destination’: ‘…’, ‘days’: …}”

ChatGPT responds in the requested format, making it programmatically parseable.

Why Do We Need Context-Rich Prompts?

Modern AI solutions not only rely on LLMs but also increasingly use AI agents. While frameworks and tools are important, the real strength of an AI agent lies in how effectively it gathers and delivers context to the LLM.

Think of it this way: the agent's primary role isn't to decide how to respond. It's about collecting the right information and extending the context before invoking the LLM. This could involve adding data from databases, APIs, user profiles, or past conversations.

When two AI agents use the same framework and tools, their real distinction lies in how instructions and context are engineered. A context-rich prompt ensures the LLM understands not only the immediate question but also the broader goal, user preferences, and any external facts necessary to produce precise, reliable results.

Example

Take, for instance, two system prompts given to an agent whose goal is to deliver a personalized diet and workout plan.

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