Prompt engineering is how you word the instruction you give a model. Context engineering is what information the model can actually see when it answers. In short: prompt engineering shapes the question, and context engineering shapes everything the model knows before it responds.
The two are not rivals. They work together, and prompt engineering is really one part of the larger job of context engineering.
The prompt is only one input among many. Everything else the model reads at answer time is context, and choosing that information is the core idea in Anthropic's context engineering guidance, which describes context engineering as "the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference, including all the other information that may land there outside of the prompts."
That framing matters because most real problems come down to inputs. The rest of this guide walks through both terms, why teams are shifting, and where good context actually comes from.
What Is Prompt Engineering?
A prompt is the input you give a model to guide its output. It is the text you type, such as a question, an instruction, or an example. Prompt engineering is the practice of refining that wording, structure, and examples to get a better answer.
Prompt engineering works inside a single interaction. You are tuning one message, not the whole system around it. It operates at the single-turn level: one input goes in, one output comes back.
This works well for bounded tasks. When the job is clear and self-contained, a sharp prompt is often all you need.
Common prompt engineering techniques
- Few-shot prompting: Show the model a few examples of the input and the output you want, so it copies the pattern.
- Chain-of-thought: Ask the model to reason step by step before giving a final answer, which helps on tricky logic.
- Role assignment: Tell the model who it should act as, such as "You are a senior tax accountant," to set the tone and depth.
- Format control: State the exact output shape you want, like a table, a list, or JSON, so the answer is easy to use.
- Clear instructions: Spell out the task, the limits, and what to avoid, since vague prompts produce vague results.
What Is Context Engineering?
Context engineering is the practice of curating everything the model sees at inference time, not just the prompt. Inference is the moment the model reads its inputs and produces an answer. So context engineering decides which facts, history, tools, and data land in front of the model before it responds.
Here is the contrast in one line. Prompt engineering asks "how should I phrase this?" Context engineering asks "what does the model need to know to get this right?"
The term itself is new. It emerged in 2025 as builders moved from simple chatbots to multi-step agents. A widely shared version comes from Andrej Karpathy's definition, which calls context engineering "the delicate art and science of filling the context window with just the right information for the next step."
Why prompt engineering became context engineering
Early apps ran on a single chatbot prompt written once at build time. You knew the task, so you tuned the wording and shipped it.
Agents changed that. An agent takes many steps, and it faces inputs you never saw when you wrote the prompt, such as a new user record, a live web page, or a tool result.
You cannot hand-write a prompt for every possible situation. So the work shifts to assembling the right context at each step. That is why prompt engineering is best seen as a subset of context engineering: the prompt is one input, and context engineering manages all of them.
Context Engineering vs Prompt Engineering: Side-by-Side
The two practices differ on almost every dimension, from what question they answer to how you debug them. The table below lines them up so the split is easy to scan.
| Dimension | Prompt Engineering | Context Engineering |
|---|---|---|
| Core question | How should I word this? | What does the model need to know? |
| Scope | A single message | Everything in the context window |
| State | Stateless, one turn at a time | Stateful across steps and sessions |
| Where knowledge lives | Inside the prompt text | In retrieved data, memory, and tools |
| Tool usage | Rare or none | Central to the design |
| Debugging approach | Reword the prompt | Fix what data reached the model |
| Best for | Bounded, single-turn tasks | Agents and data-dependent answers |
Why the Shift Is Happening Now
Prompt-only apps often shine in a demo and then break in production. Common failures include hallucinated facts, wrong tool calls, and cost that climbs as prompts grow longer.
A tempting fix is to stuff more into the context window. But more context is not automatically better. In Chroma's context rot study, a July 2025 test of 18 models found that "models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows."
The drop starts earlier than most people expect. Accuracy begins falling around 32,000 tokens for Llama 3.1 405b, and sooner for smaller models, according to a Databricks study. (A token is a chunk of text, roughly a short word or word-piece.)
Where you place information matters too. LLM performance degrades when the key information sits in the middle of a long context, and models do best when it appears at the beginning or end, per the "lost in the middle" research from Stanford.
The business stakes are real. Around 95% of generative AI pilots at large companies are failing to deliver measurable return, according to an MIT report. Better context is one of the clearest levers teams have to close that gap.
What Fills the Context Window
The context window is the full set of tokens a model can read at once. Context engineering is really the job of deciding what goes into that window at each step.
The key shift is timing. Context is assembled fresh at runtime for each request, not written once at build time. That means many moving parts feed the window.
The building blocks of context
- System instructions: The standing rules that set the model's role, goals, and limits.
- User prompt: The current request, the single-turn input you learned about above.
- Conversation history: Earlier turns in the session that keep the model on track.
- Long-term memory: Saved facts about the user or task that persist across sessions.
- Retrieved knowledge (RAG): Retrieval augmented generation pulls relevant documents into the window so the model answers from real sources, not just its training.
- Tool definitions and outputs: The tools the model can call and the data those calls return.
- Structured output format: The exact shape the answer must take, such as JSON, so downstream code can use it.
Why more tools and data can hurt
Adding tools and data feels safe, but it can quietly lower quality. Models can get overloaded and pick the wrong option.
Models sometimes call the wrong tool, and parallel tool calls are harder than simple ones, as shown by the Berkeley Function-Calling Leaderboard. Too many tools widens the decision surface, so the lesson is to curate a minimal, high-signal set instead of dumping everything into the window.
The Missing Layer: Where Good Context Comes From
Most guides stop at "retrieve and assemble" the context. They rarely cover the harder question: where does the right information actually come from in the first place?
A model's training data goes stale the moment it ships. Agents that answer real questions need current facts, such as today's price, the latest policy, or a live page, and those live on the open web. Pulling clean, current data off that web is often done with web scraping in agent workflows.
This is the data-sourcing layer of context engineering, and it is easy to overlook. One way to keep answers current is grounding agents with live web data so the model reasons over fresh, cited sources instead of guessing from old training.
Format your context, not just your prompt
How you format retrieved data changes both token cost and accuracy. Raw HTML is full of tags, scripts, and markup that eat tokens and distract the model.
Clean Markdown carries the same meaning with far less noise. In Olostep internal testing, a page's raw HTML can consume around 50,000 tokens, while the same content as Markdown uses around 5,000 tokens, roughly a 10x reduction. That is the case for feeding clean data to LLMs in a tidy format before it ever reaches the window.
A Simple Context Engineering Example
Picture a support agent answering "Can I still get a refund on order 4471?" A prompt-only version has no live data, so it guesses from general policy and may cite a rule that has since changed.
The context-engineered version does the sourcing first. It retrieves the live transaction record for order 4471 and the current refund policy, then formats both as clean Markdown before the model reads them.
Now the model sees the real purchase date, the real policy version, and the exact refund window. Its answer is grounded in facts that were fetched at runtime, not recalled from stale training.
That sourcing step is the whole difference. Scheduled, repeatable data gathering, such as the kind done by AI research agents, keeps those inputs fresh so the loop works every time, not just in a demo.
When to Use Prompt Engineering vs Context Engineering
You do not always need the heavier approach. The right choice depends on how stable and self-contained the task is.
- Reach for prompt engineering on stable, single-turn tasks like drafting an email, summarizing a document, or prototyping an idea, where the model already knows enough.
- Reach for context engineering when the answer depends on current data, user permissions, memory, or multi-step tool calls, where wording alone cannot supply the facts.
- Remember both matter: even a context-heavy agent still needs well-worded prompts, so context engineering builds on prompt engineering rather than replacing it.
How to Start Context Engineering (Checklist)
- Audit what enters your prompts: List every piece of text the model sees today, so you know your real starting point.
- Retrieve only high-signal tokens: Pull the few documents that actually answer the question, not everything you have.
- Format for tokens: Convert data to clean Markdown or JSON so it costs fewer tokens and reads clearly.
- Keep data fresh with live sources: Fetch current facts at runtime instead of relying on stale training data.
- Separate static vs dynamic context: Keep fixed rules in one place and changing data in another, so each is easy to update.
- Measure and iterate: Track accuracy and cost, then trim or add context based on what the numbers show.
A clean pipeline for sourcing that data matters as much as the model. A unified Web Data API that turns any URL into clean, structured output gives your agents a dependable feed for the checklist above.
The Bottom Line
Prompt engineering shapes the ask. Context engineering shapes what the model knows before it answers. Both are needed, but the balance is shifting.
As apps move from single prompts to production agents, context wins. The model can only reason over the information you put in front of it, so the data you feed it is the foundation.
That foundation is a data problem as much as a modeling one, which is the view behind infrastructure built for AI agents. Get the inputs right, and the rest of the system has a fighting chance.
Frequently Asked Questions
Is prompt engineering dead?
No. It is now one part of context engineering and remains essential for wording instructions and handling single-turn tasks.
Is prompt engineering a subset of context engineering?
Yes. The prompt is just one input among many that fill the context window, so shaping it is a piece of the broader context job.
What are examples of context engineering?
Common examples include RAG retrieval, agent memory, tool definitions, live web grounding, and formatting fetched data as clean Markdown before it enters the window.
Do I need context engineering for a simple chatbot?
Not always. Single-turn, bounded tasks can run on good prompts alone; you need context engineering once answers depend on current data, memory, or tools.
