What is Context Engineering? The Next Big Skill After Prompt Engineering
When an AI system gives the wrong answer, our first instinct is to blame the model. But often, the model never had a fair shot — it was working with an incomplete picture. Imagine asking a colleague to make a business forecast without giving them last quarter’s numbers, market trends, or even the product lineup. They might still try, but the result would be guesswork.
That’s the quiet reality in many AI applications today. The model isn’t failing because it’s “not smart enough.” It’s failing because we’re not giving it the right environment to think and act effectively. Context engineering is the discipline that fixes this — designing the information, tools, and structure an AI needs before it even begins the task. It’s less about clever wording and more about making sure the AI walks into the conversation fully prepared.
The Problem With Just Prompt Engineering
Prompt engineering has been the go-to skill for getting better answers from AI — tweaking the wording, adding examples, adjusting tone. It works… to a point. But it’s also a fragile strategy.
A perfectly tuned prompt might break the moment the task changes slightly, or when you switch to a new model version. It can’t pull in fresh data from your CRM, your product manuals, or your API logs. And it certainly can’t adapt on its own when the question needs more than a single block of text to be answered well.
In other words, prompt engineering focuses on how you ask, but ignores what the AI actually has to work with. For simple, static tasks, that might be fine. For anything dynamic or complex, it’s like trying to win a chess match when half your pieces are missing from the board.
What is Context Engineering?
Context engineering is about preparing an AI system so it can actually succeed at the task you give it. Instead of relying on a single, static prompt, it focuses on building a system that assembles all the relevant pieces — data, tools, history, and instructions — before the model even starts generating an answer.
Think of it less as “writing the perfect question” and more as setting the stage. That might mean pulling recent customer emails, retrieving product specifications, adding the right business rules, and formatting it all so the AI can make sense of it. The result is a model that’s not just guessing based on memory, but reasoning with the right context in hand.
Example: Imagine a support chatbot for an airline. Without context engineering, it might only see the passenger’s typed question:
“Can I change my flight to next Monday?”
The model could respond with a generic policy answer, but it wouldn’t know if the passenger’s ticket allows changes, whether there’s a seat available, or if there’s a fee. With context engineering, the system would first pull up the booking details, check the fare rules, query seat availability, and then feed all that into the model. The AI could then give a personalised, accurate answer — something like:
“Yes, you can change to the 10:30 AM flight on Monday. There’s no change fee, but the fare difference is $45.”
Where prompt engineering stops at the front door, context engineering makes sure the AI has the full floor plan, the keys, and a map of where to go.
Core Principles of Context Engineering
Context engineering isn’t just “dump more data into the prompt.” It’s a deliberate process of curating, structuring, and delivering the exact information and capabilities an AI system needs to complete a task — nothing extra, nothing missing. Below are the key principles that make it work in practice.
1. Dynamic Context Assembly
Context should be assembled in real time, based on the task. A fixed, one-size-fits-all context risks either leaving out crucial details or overloading the model with noise.
Example
Without dynamic assembly: A customer support AI always receives a full 50-page policy document before answering a refund query.
With dynamic assembly: The system first detects the intent (“refund request”), retrieves the specific policy section on refunds, and includes only that alongside the order history
Benefit: The AI can focus on what matters, respond faster, and reduce token costs.
2. Right Information + Right Tools
An AI often needs both facts and functions.
Information: Knowledge bases, databases, conversation history, documentation.
Tools: APIs, calculators, search functions, translation services.
Example: A travel planning agent might need:
Real-time flight schedule API (tool)
Destination travel advisories (information)
Currency conversion API (tool)
User preferences from past trips (information)
Without both, even the smartest model will make inaccurate suggestions.
3. Relevance Over Volume
More context isn’t automatically better. Irrelevant or tangential information can distract the model and degrade performance.
Example: In a legal contract review tool, including every clause of unrelated contracts may cause the AI to misinterpret obligations. Instead, retrieval should be scoped to clauses related to the specific legal question.
Tip: Using vector search with filters helps target only the most relevant chunks.
4. Formatting Matters
The same information can produce different results depending on how it’s presented. Good formatting turns raw data into model-friendly inputs.
Example:
Poor formatting: “Customer order history: {messy JSON dump with no clear keys}”
Better formatting:
Customer: John Doe
Order ID: 12345
Item: Noise-Cancelling Headphones
Purchase Date: 2023-07-12
Warranty: 2 years
Status: ActiveThis clarity helps the AI parse and use the data effectively.
5. Temporal Awareness
Information loses value over time. Good context engineering ensures the model works with up-to-date data, not outdated snapshots.
Example: A stock market analysis agent should always pull the latest price and earnings data before making investment recommendations. Using data from even a week ago could lead to incorrect calls.
6. Layered Context
Some tasks require multiple layers of information — immediate task-specific data, persistent background knowledge, and broader world context.
Example:
For a personal shopping assistant:
Layer 1: Current shopping cart contents (task-specific)
Layer 2: User’s clothing size preferences (persistent)
Layer 3: Current season and fashion trends (world context)
Layering helps the AI balance short-term and long-term relevance.
7. Minimising Cognitive Load
Just like humans, models perform better when they don’t have to sift through excessive detail to find what’s important.
Example: A bug-fixing code agent shouldn’t receive an entire monorepo if the bug is in one function. Give it only the relevant file, related imports, and error logs.
8. Maintaining Context Across Turns
For multi-step interactions, it’s not enough to supply context once — you must maintain and update it as the conversation or task evolves.
Example: In a medical triage chatbot, the model should remember the patient’s reported symptoms from earlier in the conversation when suggesting possible next steps.
9. Context Validation Before Execution
Always check if the assembled context actually makes sense for the task before giving it to the model.
Example: A meeting summarizer might accidentally pull the wrong meeting transcript because of a naming clash. A validation step could confirm participants or keywords match before sending it to the AI.
By following these principles, context engineering transforms an AI system from a one-shot prompt responder into a reliable, situationally aware assistant.
Examples in Action
Principles are useful, but context engineering really clicks when you see it operating inside real systems. Here are some examples that span different industries and AI architectures.
1. Retrieval-Augmented Generation (RAG) for Customer Support
Scenario: A telecom company uses an AI assistant to handle customer queries about internet outages.
How Context Engineering Works Here:
The system detects the customer’s intent (e.g., “internet not working”).
It dynamically retrieves relevant troubleshooting steps from a technical knowledge base.
It checks the customer’s account for active outages or unpaid bills.
It formats the information into a concise, model-ready input
Customer: Jane Smith
Account Status: Active, No Pending Payments
Location: New York, ZIP 10027
Current Issue: Internet outage
Known Outage in Area: Yes, estimated fix by 3:00 PM ESTThe AI responds with a personalised message, combining empathy and precise next steps.
2. Multi-Agent Architecture for Legal Research
Scenario: A law firm deploys a system of AI agents to prepare case briefs.
How Context Engineering Works Here:
Research Agent retrieves relevant precedents from a legal database.
Summarisation Agent condenses long judgments into case-relevant excerpts.
Synthesis Agent integrates case law with client details.
All context is validated for jurisdiction and date before reaching the final model.
Output is a brief that’s both accurate and tailored to the case at hand.
Benefit: Avoids irrelevant cases and outdated precedents, while ensuring structured, consistent results.
3. AI-Driven Personal Finance Advisor
Scenario: A banking app uses AI to give personalised savings advice.
How Context Engineering Works Here:
The AI pulls the user’s last six months of transactions, recurring bills, and savings goals.
It connects to market data APIs for current interest rates.
Context is layered:
Immediate: Current balance and upcoming bills.
Persistent: Long-term savings goal of $20,000 for a home down payment.
External: Latest bank product offers.
The AI suggests a savings plan, grounded in real data rather than generic budgeting tips.
4. Medical Triage Assistant
Scenario: A hospital uses AI to pre-screen patient symptoms before doctor consultations.
How Context Engineering Works Here:
Patient history is retrieved from the hospital’s electronic health record system.
Current reported symptoms are captured through a structured intake form.
Relevant clinical guidelines are loaded for the patient’s age group and medical history.
The AI produces a risk assessment and suggests next steps for the attending nurse.
Impact: The AI doesn’t just guess — it operates with the patient’s full medical picture.
5. Code Fixing Assistant for DevOps
Scenario: A developer pastes an error log into an AI-powered bug-fixer tool.
How Context Engineering Works Here:
The system detects the file and function involved from the stack trace.
It pulls only the relevant portion of the source code and related configs from the repo.
It includes recent commit history for that file to give context on changes.
The AI proposes a fix that aligns with recent codebase updates.
Bottom line: In each case, the AI is only as good as the environment it’s placed in. Context engineering builds that environment so the AI doesn’t work in the dark.
How to Start Practicing Context Engineering
You don’t need a massive AI infrastructure to begin with context engineering. The process can start small and grow as your needs get more complex. Here’s a practical roadmap:
Step 1: Define the Task Clearly
Before thinking about inputs, clarify exactly what the AI should deliver.
Is it answering a factual question, generating a plan, writing content, or making a decision?
What does a good answer look like?
Example: For an internal HR bot, the task could be: “Answer employee questions about leave policies accurately, with references to official policy documents.”
Step 2: Map the Required Context
Identify all the information and tools the AI would need to do the job well.
What facts are essential?
What tools or APIs are needed?
Which sources are authoritative?
Example: For the HR bot:
Leave policy document (information)
Employee’s leave balance from HRMS (information)
Calendar API to check dates (tool)
Step 3: Decide How to Retrieve the Context
Context should be fetched automatically, not manually. Common strategies:
Retrieval-Augmented Generation (RAG) for document search.
Database queries for structured data.
API calls for real-time information.
Tip: Use retrieval filters to avoid pulling irrelevant or outdated content.
Step 4: Format the Context for the Model
Structure matters. Convert raw data into a clean, model-friendly format:
Use bullet points for key facts.
Keep tool outputs in consistent JSON or table formats.
Summarise long texts into concise, relevant chunks.
Example:
User: Jane Doe
Leave Balance: 12 days annual leave, 5 days sick leave
Policy: Employees can carry over up to 5 days of unused annual leave to next year.
Question: "Can I carry over 10 days?"Step 5: Layer the Context
Split inputs into:
Task-specific (current request data)
Persistent (user profile, history)
Global (policies, rules, common facts)
This avoids repeating irrelevant information for every query while keeping important long-term context available.
Step 6: Validate Before Sending
Ensure the assembled context is correct and relevant.
Check that retrieved data matches the user’s intent.
Verify timestamps for freshness.
Example: If a policy document was last updated 3 years ago, flag it for review before use.
Step 7: Test and Iterate
Run the AI with different context setups and measure results.
Does more context improve accuracy?
Are there cases where less context performs better?
Track metrics like accuracy, response relevance, and user satisfaction.
Step 8: Automate the Pipeline
Once you know what works, build a repeatable pipeline
Context retrieval → Formatting → Validation → Model input
Use frameworks like LangChain, LlamaIndex, or DSPy for orchestration.
Tools & Frameworks That Help
Context engineering often needs more than clever code — it benefits from frameworks designed to orchestrate retrieval, formatting, and tool use. Here are some that are shaping the space right now:
1. LangChain / LangGraph
Best for: Building multi-step workflows and chaining together retrieval, reasoning, and tool usage.
How it helps
Pre-built integrations with vector databases for RAG.
Built-in prompt templates for consistent formatting.
LangGraph allows branching logic and stateful interactions for dynamic context assembly.
Example: A customer support system that retrieves account info, fetches relevant policy documents, and passes them to the LLM in a clean, structured prompt.
2. LlamaIndex
Best for: Document-heavy applications where precise retrieval matters.
How it helps:
Flexible indexing options (tree, list, vector) to suit different retrieval needs.
Can summarise or chunk documents before feeding them to the model.
Example: A compliance assistant that pulls the exact sections of legal texts relevant to a query and formats them for the AI to interpret.
3. DSPy
Best for: Programmatic prompt and context optimisation.
How it helps:
Lets you define “skills” as functions with dynamic context.
Can auto-tune retrieval and formatting strategies.
Example: An analytics agent that automatically tests different ways of structuring sales data before analysis to find the most accurate results.
4. Microsoft AutoGen
Best for: Multi-agent systems where each agent has specialised context.
How it helps:
Orchestrates conversations between agents.
Supports tool-calling and shared memory between agents.
Example: A research assistant system where one agent fetches relevant papers, another summarises them, and a third synthesises them into a literature review.
5. Model Context Protocol (MCP)
Best for: Standardising how tools and data sources connect to AI systems.
How it helps:
Defines a universal way for LLMs to access structured data and APIs.
Makes it easier to build portable context pipelines across different AI platforms.
Example: An enterprise chatbot that can query multiple internal databases using a consistent API interface, regardless of the underlying system.
6. Agno
Best for: Modular AI apps with lightweight orchestration.
How it helps:
Quick to set up for small-scale but production-ready apps.
Good for embedding context retrieval and processing logic in clean, reusable components.
Example: A personalised email assistant that retrieves a user’s past correspondence before drafting replies.
Tip: You don’t need to pick just one. Many production systems combine these — e.g., LlamaIndex for retrieval, LangChain for orchestration, MCP for integration.
Why This Matters for the Future of AI
As AI models become more capable, their performance bottleneck is shifting away from the model itself and toward the quality of the context we give them. A model like GPT-5 or Claude 3.5 can already write, reason, and plan impressively — but without the right context, it’s still guessing.
In real-world deployments, the difference between a “wow” moment and a frustrating failure often comes down to whether the model had the right data, tools, and instructions at the right time. That’s what context engineering delivers.
Better grounding: By pulling from verified, relevant sources, context engineering reduces hallucinations and boosts factual accuracy.
Adaptability: Dynamic context pipelines make AI systems resilient to task changes, model updates, and data shifts.
Scalability: You can run the same AI logic across thousands of different cases without manually rewriting prompts for each one.
Enterprise-readiness: Businesses can trust AI outputs when they know every response is built on validated, up-to-date context.
The shift from prompt engineering to context engineering mirrors earlier tech transitions: from writing one-off scripts to building full software systems, from isolated databases to integrated data pipelines. In each case, the winners weren’t just the people who could “make it work,” but those who could make it work reliably and repeatedly at scale.
In the next few years, context engineers will be as essential to AI teams as software architects are to engineering teams today.
Conclusion
When an AI system falls short, it’s tempting to treat the model as the problem. But in many cases, the real issue is that we’ve sent it into the task blind — without the facts, tools, or framing it needs to succeed.
Context engineering changes that. It’s the discipline of building the environment around the model so it can perform at its best — retrieving the right data, integrating the right tools, structuring inputs clearly, and keeping everything relevant to the task at hand.
Prompt engineering will still have its place, but it’s no longer enough on its own. The future of AI will belong to those who can design for context — turning powerful models into consistently reliable systems.
If you want AI that doesn’t just answer, but understands and acts with precision, start thinking less about what you ask and more about what you give it to work with. That’s context engineering in action.