Context Engineering is the New Vibe Coding (Learn this Now)

Until last year, prompt engineering was considered an essential skill to talk with LLMs. Of late, LLMs have made tremendous headway in their reasoning and understanding capabilities. Needless to say, our expectations have also drastically scaled. A year back, we were happy if ChatGPT could write a nice email for us. But now, we want it to analyze our data, automate our systems, and design pipelines. However, prompt engineering alone is insufficient for producing scalable AI solutions. To leverage the full power of LLMs, experts are now suggesting the addition of context-rich prompts that yield reasonably accurate, reliable, and appropriate outputs, a process that is now known as “Context Engineering.” In this blog, we will understand what context engineering is, how it is different from prompt engineering. I will also share how production-grade context-engineering helps in building enterprise-grade solutions.

What is Context Engineering?

Context engineering is the process of structuring the entire input provided to a large language model to enhance its accuracy and reliability. It involves structuring and optimizing the prompts in a way that an LLM gets all the “context” that it needs to generate an answer that exactly matches the required output. 

Context Engineering vs Prompt Engineering

At first, it may seem like context engineering is another word for prompt engineering. But is it not? Let’s understand the difference quickly, 

Prompt engineering is all about writing a single, well-structured input that will guide the output received from an LLM. It helps to get the best output using just the prompt. Prompt engineering is about what you ask. 

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

Basically,

Context Engineering = Prompt Engineering + (Documents/Agents/Metadata/RAG, etc.)

What are the components of Context Engineering?

Context engineering goes way beyond just the prompt. Some of its components are:

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

Each component of the context shapes the way LLM processes the input, and it works accordingly. Let’s understand each of these components and illustrate this further using ChatGPT.

1. Instruction Prompt

Instructions/System Prompts to guide the model’s personality, rules, and behavior.

How ChatGPT utilizes it?

It “frames” all future 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 not give 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/questions.

How ChatGPT utilizes it?

It is the primary signal for what response to generate. 

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

3. Conversation History

Conversation History to maintain flow.

How ChatGPT utilizes it?

It reads the entire chat so far every time it responds, to remain consistent.

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

4. Long-term Memory

Long-term memory is for maintaining 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 choices. 

5. RAG

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

In ChatGPT with browsing/tools enabled: 

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

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

6. Tool Definition

Tool Definitions so that the model knows 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 gives you real flight options. 

7. Output Structure

Structured Output formats will respond 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 format you asked for so that it is programmatically parsable.

Why Do We Need Context-Rich Prompts?

Modern AI solutions will not only use LLMs, but AI agents are also becoming very popular to use. While frameworks and tools matter, the true power of an AI agent comes from how effectively it gathers and delivers context to the LLM.

Think of it this way: the agent’s primary job isn’t deciding how to respond. It’s about collecting the right information and extending the context before calling the LLM. This could mean adding data from databases, APIs, user profiles, or prior conversations.

When two AI agents use the same framework and tools, their real difference 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 it needs to produce precise, reliable results.

Example

For example, consider two system prompts provided to an agent whose goal is to deliver a personalized diet and workout plan.

Using the Well-Structured Prompt

The agent acts like a professional coach. 

•  Asks questions one at a time, in perfect sequence. 
•  Never generate an action plan until it is ready to do so. 
•  Validates, confirms, and provides acknowledgement for every user input. 
• Will only provide a detailed, safe, and personalized action plan after it has collected everything. 

Overall, the user experience feels fully professional, reliable, and safe!

With an Unstructured Prompt

• The agent could start by giving a plan and no information.
• The user might say, “Make me a plan!” and the agent may provide a generic plan with no thought whatsoever.
• No assessment for age, injuries, or dietary restrictions → consideration for the highest chance of unsafe information.
• The conversation might degrade into random questions, with no structure.
• No guarantees about sufficient and safe information.
• User experience is lower than what could be professional and even safer.

In short, context engineering transforms AI agents from basic chatbots into powerful, purpose-driven systems.

How to Write Better Context-Rich Prompts for Your Workflow?

After recognizing why context-rich prompts are necessary comes the next critical step, which is designing workflows that allow agents to collect, organize, and provide context to the LLM. This comes down to four core skills: Writing Context, Selecting Context, Compressing Context, and Isolating Context. Let’s break down what each means in practice.

Develop Writing Context

Writing context means assisting your agents in capturing and saving relevant information that may be useful later. Writing context is similar to a human taking notes while attempting to solve a problem, so that they do not need to hold every detail at once in their head.

For example, within the FitCoach example, the agent does not just ask a question to the user and forgets what the user’s answer is. The agent records (in real-time) the user’s age, target, diet preferences, and other facts during the conversation. These notes, also referred to as scratchpads, exist outside of the immediate conversation window, allowing the agent to review what has already occurred at any point in time. Written context may be stored in files, databases, or runtime memory, but written context ensures the agent never forgets important facts during the development of a user-specific plan.

Selecting Context

Gathering information is only valuable if the agent can find the right bits when needed. Imagine if FitCoach remembered every detail of all users, but couldn’t find the details just for one user. 

Selecting context is precisely about bringing in just the relevant information for the task at hand. 

For example, when FitCoach generates a workout plan, it must select task context details that include the user’s height, weight, and activity level, while ignoring all of the irrelevant information. This may include selecting some identifiable facts from the scratchpad, while also retrieving memories from long-term memory, or relying on examples that identify how the agent should behave. It is through selective memory that agents remain focused and accurate.

Compressing Context

Occasionally, a conversation grows so long that it exceeds the LLM’s memory window. This is when we compress context. The aim is to reduce the information to the smallest size possible while keeping the salient details.

Agents typically accomplish this by summarizing earlier parts of the conversation. For example, after 50 messages of back and forth with a user, FitCoach could summarize all of the information into a few concise sentences:

“The user is a 35-year-old male, weighing 180 lbs, aiming for muscle gain, moderately active, no injury, and prefers a high protein diet.”

In this manner, even though the conversation may have extended over hundreds of turns, the agent could still fit key facts about the user into the LLM’s significantly sized context window. Recursively summarizing or summarizing at the right breakpoints when there are logical breaks in the conversation should allow the agent to stay efficient and ensure that it retains the salient information.

Isolate Context

Isolating context means breaking down information into separate pieces so a single agent, or multiple agents, can better undertake complex tasks. Instead of cramming all knowledge into one massive prompt, developers will often split context across specialized sub-agents or even sandboxed environments. 

For example, in the FitCoach use case, one sub-agent could be focused on purely collecting workout information, while the other is focused on dietary preferences, etc. Each sub-agent is operating in its slice of context, so it doesn’t get overloaded, and the conversation can stay focused and purposeful. Similarly, technical solutions like sandboxing allow agents to run code or execute an API call in an isolated environment while only reporting the important outcomes to the LLM. This avoids leaking unnecessary or potentially sensitive data to the main context window and gives each part of the system only the information it strictly needs: not more, not less.

Also Read: Learning Path to Become a Prompt Engineering Specialist

My Advice

Writing, selecting, compressing, and isolating context: these are all foundational practices for AI agent design that is production-grade. These practices will help a developer operationalize AI agents with safety, accuracy, and intent for user question answering. Whether creating a single chatbot or an episodic swarm of agents running in parallel, context engineering will elevate AI from an experimental plaything into a serious tool capable of scaling to the demands of the real world.

Conclusion

In this blog, I shared my experience from prompt engineering to context engineering. Prompt engineering alone won’t provide the basis for building scalable, production-ready solutions in the changing AI landscape. To truly extract the capabilities provided by modern AI, constructing and managing the entire context system that surrounds an LLM has become paramount. Being intentional about context engineering has driven my ability to maintain prototypes as robust enterprise-grade applications, which has been critical for me as I make my pivot from prompt-based tinkering into context-driven engineering. I hope sharing a glimpse of my journey helps others scale their progress from prompt-driven engineering to context engineering.