Prompt Engineering vs Context Engineering

From crafting single prompts to designing dynamic systems that give LLMs everything they need to succeed

Published

August 10, 2025

Keywords: context engineering, prompt engineering, LLM applications, RAG, few-shot examples, tool use, context window, agent design, system prompts, memory management

Introduction

For years, prompt engineering has been the go-to skill for working with LLMs — crafting the perfect instruction to get the best output. But as LLM applications evolve from simple chatbots to complex agentic systems, a new discipline is emerging: context engineering.

The shift is significant. Prompt engineering focuses on what you say to the model. Context engineering focuses on everything the model sees before it generates a response — and building the systems that assemble that context dynamically.

“Context engineering is the art of providing all the context for the task to be plausibly solvable by the LLM.” — Tobi Lutke (CEO, Shopify)

“In every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step.” — Andrej Karpathy

This article breaks down both disciplines, compares them, and shows how context engineering builds on prompt engineering to create production-grade LLM applications.

graph LR
    A["Prompt Engineering<br/>(craft the instruction)"] --> B["Context Engineering<br/>(design the full context)"]
    B --> C["Production-grade<br/>LLM applications"]

    style A fill:#ffce67,stroke:#333
    style B fill:#6cc3d5,stroke:#333,color:#fff
    style C fill:#56cc9d,stroke:#333,color:#fff

What is Prompt Engineering?

Prompt engineering is the practice of designing and refining the text input (prompt) sent to an LLM to elicit the desired output. It operates primarily at the instruction level.

Core Techniques

Technique	Description	Example
Zero-shot	Direct instruction, no examples	“Summarize this article in 3 bullet points.”
Few-shot	Include examples in the prompt	“Here are 3 examples of good summaries…”
Chain-of-thought	Ask the model to reason step by step	“Think step by step before answering.”
Role prompting	Assign a persona or role	“You are a senior Python developer…”
Output formatting	Specify the desired format	“Return your answer as a JSON object with…”

Strengths

Simple and accessible — anyone can iterate on prompts
No infrastructure required — works directly in a chat interface
Fast iteration cycle — change text, see results immediately
Well-documented with established best practices

Limitations

Static: The prompt is the same regardless of the user or context
Doesn’t scale: As tasks get complex, prompts become unwieldy
Information gap: The model only knows what’s in the prompt — no access to external data, tools, or state
Context window waste: Manual inclusion of examples and context is inefficient

What is Context Engineering?

Context engineering is the discipline of designing and building dynamic systems that provide the right information, tools, and state — in the right format, at the right time — to give an LLM everything it needs to accomplish a task.

It’s not just about writing a better prompt. It’s about building the entire input pipeline that feeds the model.

graph TD
    A["Context Engineering"] --> B["Instructions /<br/>System Prompt"]
    A --> C["User Prompt"]
    A --> D["State / History<br/>(Short-term Memory)"]
    A --> E["Long-term Memory<br/>(Cross-session)"]
    A --> F["Retrieved Information<br/>(RAG)"]
    A --> G["Available Tools<br/>(Functions, APIs)"]
    A --> H["Structured Output<br/>(Format definitions)"]

    style A fill:#56cc9d,stroke:#333,color:#fff
    style B fill:#6cc3d5,stroke:#333,color:#fff
    style C fill:#6cc3d5,stroke:#333,color:#fff
    style D fill:#6cc3d5,stroke:#333,color:#fff
    style E fill:#6cc3d5,stroke:#333,color:#fff
    style F fill:#6cc3d5,stroke:#333,color:#fff
    style G fill:#6cc3d5,stroke:#333,color:#fff
    style H fill:#6cc3d5,stroke:#333,color:#fff

The Seven Components of Context

Instructions / System Prompt: Rules, persona, constraints, and few-shot examples that define the model’s behavior.
User Prompt: The immediate task or question from the user.
State / History (Short-term Memory): The current conversation history — user messages and model responses that led to this point.
Long-term Memory: Persistent knowledge across sessions — user preferences, past interactions, learned facts.
Retrieved Information (RAG): External, up-to-date knowledge pulled from documents, databases, or APIs to answer specific questions. See our tutorials on building RAG systems for implementation details.
Available Tools: Definitions of functions the model can call (e.g., search_docs, send_email, run_code). For tool-using agents, see our agents section.
Structured Output: Format specifications that constrain the model’s response (JSON schemas, enums, etc.).

Key Principles

Context engineering is:

A system, not a string: Context is the output of a dynamic pipeline, not a static template.
Dynamic: Assembled on the fly based on the current task — calendar data for scheduling, code files for debugging, search results for research.
Selective: The right information at the right time. Not everything, but exactly what’s needed.
Format-aware: How you present information matters. A concise summary beats a raw data dump.

Prompt Engineering vs Context Engineering

graph LR
    subgraph pe["Prompt Engineering"]
        direction TB
        P1["Static prompt template"]
        P2["Manual few-shot examples"]
        P3["Single LLM call"]
    end
    subgraph ce["Context Engineering"]
        direction TB
        C1["Dynamic context assembly"]
        C2["RAG + Memory + Tools"]
        C3["Multi-step agent systems"]
    end

    pe -->|"evolves into"| ce

    style pe fill:#ffce67,stroke:#333
    style ce fill:#56cc9d,stroke:#333,color:#fff

Dimension	Prompt Engineering	Context Engineering
Focus	Crafting the instruction text	Designing the full input pipeline
Nature	Static, template-based	Dynamic, system-based
Scope	Single prompt / LLM call	End-to-end context assembly
Information	Manually included in prompt	Dynamically retrieved (RAG, APIs, memory)
Tools	None — text only	Function calling, MCP, tool schemas
State	Stateless or manual history	Managed conversation + long-term memory
Complexity	Low — text editing	High — infrastructure + engineering
Best for	Simple tasks, prototyping	Production agents, complex applications
Failure mode	Bad output from bad instructions	Agent failure from missing context

Why Context Engineering Matters for Agents

The rise of AI agents — systems that autonomously plan, use tools, and iterate — makes context engineering essential. Agent failures are increasingly not model failures, but context failures.

graph TD
    A["Agent receives task"] --> B["Context Engineering<br/>System assembles context"]
    B --> C["Fetch relevant docs<br/>(RAG)"]
    B --> D["Load conversation<br/>history"]
    B --> E["Inject available<br/>tools"]
    B --> F["Retrieve user<br/>preferences"]
    C --> G["LLM generates<br/>response / action"]
    D --> G
    E --> G
    F --> G
    G --> H["Execute tools /<br/>return response"]
    H -->|"loop"| B

    style B fill:#6cc3d5,stroke:#333,color:#fff
    style G fill:#56cc9d,stroke:#333,color:#fff
    style H fill:#ffce67,stroke:#333

Consider an AI assistant asked to schedule a meeting from an email:

With prompt engineering only (poor context):

“Thank you for your message. Tomorrow works for me. May I ask what time you had in mind?”

With context engineering (rich context — calendar, contacts, email history, tools):

“Hey Jim! Tomorrow’s packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if it works.”

The second response is possible because the system engineered the context — it loaded the user’s calendar, identified the sender from the contact list, checked the email history for tone, and had access to a send_invite tool.

Practical Context Engineering Techniques

1. Retrieval-Augmented Generation (RAG)

Dynamically retrieve relevant documents or data before the LLM call.

# Pseudocode: RAG-powered context
query = user_message
relevant_docs = vector_store.similarity_search(query, k=5)
context = "\n".join([doc.page_content for doc in relevant_docs])

messages = [
    {"role": "system", "content": f"Use this context to answer:\n{context}"},
    {"role": "user", "content": query},
]

For hands-on RAG tutorials, see our RAG section.

2. Conversation History Management

Maintain short-term memory with intelligent truncation or summarization.

# Keep recent messages + summarize older ones
if len(history) > MAX_MESSAGES:
    summary = llm.summarize(history[:len(history) - KEEP_RECENT])
    history = [{"role": "system", "content": f"Summary: {summary}"}] + history[-KEEP_RECENT:]

3. Tool / Function Definitions

Give the model capabilities, not just knowledge.

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_docs",
            "description": "Search internal documentation",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    }
]

4. Dynamic System Prompts

Tailor instructions based on the current task type.

def build_system_prompt(task_type, user_profile):
    base = "You are a helpful AI assistant."
    if task_type == "coding":
        base += " You are an expert Python developer. Always include type hints."
    if user_profile.get("prefers_concise"):
        base += " Keep responses brief and to the point."
    return base

5. Few-shot Example Selection

Instead of static examples, dynamically select the most relevant ones.

# Select examples similar to the current query
examples = example_store.similarity_search(user_query, k=3)
formatted = "\n".join([f"Q: {e.question}\nA: {e.answer}" for e in examples])

Context Engineering in Practice: The Full Picture

A production LLM application combines all these components into a context assembly pipeline:

graph TD
    A["User Request"] --> B["Context Assembly Pipeline"]
    B --> C["1. Route / classify<br/>the request"]
    C --> D["2. Retrieve relevant<br/>documents (RAG)"]
    D --> E["3. Load conversation<br/>history + memory"]
    E --> F["4. Select tools<br/>for this task"]
    F --> G["5. Build system prompt<br/>(dynamic)"]
    G --> H["6. Select few-shot<br/>examples"]
    H --> I["7. Assemble final<br/>context"]
    I --> J["LLM Call"]
    J --> K["Response / Tool Call"]
    K -->|"Tool result"| B

    style B fill:#6cc3d5,stroke:#333,color:#fff
    style I fill:#ffce67,stroke:#333
    style J fill:#56cc9d,stroke:#333,color:#fff

This pipeline runs before every LLM call in a production system. The LLM itself is often the simplest part — the real engineering is in everything that feeds it.

When to Use What

graph TD
    Start["What are you building?"] --> Simple{"Simple task?<br/>(Q&A, summarization)"}
    Start --> Complex{"Complex app?<br/>(RAG, multi-step)"}
    Start --> Agent{"Autonomous agent?<br/>(Planning, tools)"}

    Simple -->|"Yes"| PE["Prompt Engineering<br/>is sufficient"]
    Complex -->|"Yes"| CE["Context Engineering<br/>required"]
    Agent -->|"Yes"| CE_ADV["Advanced Context<br/>Engineering essential"]

    style PE fill:#ffce67,stroke:#333
    style CE fill:#6cc3d5,stroke:#333,color:#fff
    style CE_ADV fill:#56cc9d,stroke:#333,color:#fff

Use prompt engineering when:

Building a prototype or quick experiment
The task is self-contained (summarization, translation, classification)
All needed information fits naturally in a single prompt
No external data or tool access is required

Use context engineering when:

Building production applications with real users
The model needs access to external data (RAG), tools, or user state
You’re building multi-turn conversational systems
You’re building agents that plan and act autonomously
Context quality directly determines success or failure

Conclusion

Prompt engineering isn’t going away — it’s the foundation. But for production LLM applications, context engineering is the real skill. The difference between a demo and a production-grade application is rarely the model or the prompt — it’s the quality of the context you provide.

The key insight: most agent failures are context failures, not model failures. When an LLM gives a bad response, the first question should be: “Did it have everything it needed?”

Context engineering is:

A system, not a string — dynamic pipelines that assemble context on the fly
Selective — the right information at the right time, not everything at once
The bridge between prompt engineering and production AI

References

Lutke, T. (2025). “Context engineering” over “prompt engineering”. X (Twitter).
Karpathy, A. (2025). +1 for “context engineering”. X (Twitter).
Willison, S. (2025). Context engineering. Simon Willison’s Weblog.
Schmid, P. (2025). The New Skill in AI is Not Prompting, It’s Context Engineering. philschmid.de.
Anthropic (2024). Building Effective Agents. Anthropic Research.
LangChain (2025). The Rise of Context Engineering. LangChain Blog.

Build a RAG pipeline with dynamic context assembly — see our RAG tutorials.
Explore agent patterns with tool use — see our Agents section.
Deploy your LLM application with vLLM or llama.cpp.
Compress models for efficient serving with quantization techniques.
Run models locally with Ollama for private, zero-latency inference.

Introduction

What is Prompt Engineering?

Core Techniques

Strengths

Limitations

What is Context Engineering?

The Seven Components of Context

Key Principles

Prompt Engineering vs Context Engineering

Why Context Engineering Matters for Agents

Practical Context Engineering Techniques

1. Retrieval-Augmented Generation (RAG)

2. Conversation History Management

3. Tool / Function Definitions

4. Dynamic System Prompts

5. Few-shot Example Selection

Context Engineering in Practice: The Full Picture

When to Use What

Conclusion

References

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