What is the difference between prompt engineering and context engineering?

Prompt Engineering vs Context Engineering

These two disciplines are related but distinct approaches to getting better results from AI models.

Definitions

Prompt Engineering

Prompt engineering is the art and science of crafting inputs to elicit better outputs.

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# Weak prompt (no engineering)
response = llm.invoke("Fix this code")

# Engineered prompt
response = llm.invoke('''
You are a senior Python engineer. Review and fix the following code.

Requirements:
- Preserve all existing functionality
- Add type hints
- Fix any bugs you find
- Explain each change you made

Code to fix:
```python
def calc(x,y):
    return x/y

''')

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**Techniques:**
* Chain-of-thought: "Think step by step"
* Few-shot: Provide examples of desired output
* Role prompting: "You are an expert..."
* Output formatting: "Return as JSON with fields: name, score"
* Constraint specification: "In under 100 words"

### Context Engineering

**Context engineering** is the discipline of designing what information flows into the model's context window — and how.

```python
# Poor context engineering: dump everything
response = llm.invoke(f"Here are all 500 documents: {all_docs}\n\nAnswer: {question}")

# Good context engineering: curated, structured context
# 1. Retrieve only relevant chunks (RAG)
relevant_chunks = vector_db.search(question, k=3)

# 2. Structure context clearly
context = f'''
<background>
{company_profile}
</background>

<relevant_documents>
{relevant_chunks}
</relevant_documents>

<conversation_history>
{last_3_turns}
</conversation_history>

<current_question>
{question}
</current_question>
'''
response = llm.invoke(context)

Techniques:

• RAG: Retrieve relevant documents dynamically
• Conversation summarization: Compress history to fit context
• Hierarchical context: Most important info first
• Structured separators:
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  <documents>

  ,
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  <instructions>

  ,
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  <examples>

  tags
• Context caching: Cache repeated context (system prompts, docs)

Which Matters More?

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Context Engineering ≥ Prompt Engineering

As AI models have gotten better at following instructions, the quality of what information you provide often matters more than how you phrase the question. However, both work together:

Evolving Landscape

"The era of prompt engineering is giving way to context engineering — the real skill is building systems that put the right information in front of the model at the right time." — 2025 AI engineering trend

With models like Claude having 200K token context windows, the challenge is less about phrasing and more about intelligently managing what goes into that window.