Paweł Huryn avatar

Paweł Huryn

@PawelHuryn

7/28/2025, 9:09:01 PM

Context engineering is the new prompt engineering.

And it’s becoming the most critical AI skill.

Together with @MiqJ  (Product Lead at @OpenAI) we created a comprehensive guide.

Key insights: 🧵👇 
1. What Is Context Engineering

It is the art and science of building systems that fill LLM context window to improve their performance.

Unlike prompt engineering, context engineering is a broader term with many activities that happen also before the prompt is even created. 
2. Types of Context

There are 6 types of context:
- Instructions
- Examples
- Knowledge
- Memory
- Tool results
- Tools 
I prepared two examples you can analyze (GitHub):

- PM Agent: https://github.com/phuryn/examples/blob/main/prompts/context_engineering/experienced_PM_context.json
- Lovable Bug Fixing Agent: https://github.com/phuryn/examples/blob/main/prompts/context_engineering/bug_fixing_agent_context.xml
Before we continue, I recommend his AI Product Management Certification. It’s a 6-week cohort taught by Miqdad Jaffer.

The next session starts on Sep 15, 2025. A special discount for our community: https://bit.ly/aipmcohort 
3. What Is RAG and Where It Fits In

RAG is often labeled a context engineering technique. But RAG is a three-step pipeline:

- Information Retrieval: Pulling data from external sources (e.g. vector DBs, APIs)

- Context Assembly: Structuring and filtering the retrieved data into a prompt

- Generation: Using an LLM (or agent) to generate the output

Context engineering doesn't include generation.
4. Information Retrieval Techniques

You can see a free RAG simulator: https://x.com/PawelHuryn/status/1945182098190401605
5. Context Assembly Techniques

Aim to provide minimal, relevant, and well-structured information.

Good context engineering requires a mix of retrieval and assembly techniques. 
@n8n_io example: 
Find this helpful?

See our full post: https://www.productcompass.pm/p/context-engineering
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