Prompt engineering was the beginning. Context engineering is the next level.

As AI agents become central to how we build products, a new discipline is emerging as the most important one for any AI engineer: knowing exactly what information to put in front of the model and when.

That's context engineering, plain and simple.

It's not just RAG. It's bigger than that.

Retrieval-Augmented Generation was a huge leap forward. But context engineering asks a harder question: given a limited context window, what's the best possible combination of information an LLM needs to execute this specific task, right now?

That includes:

• The system prompt and instructions

• User input

• Recent conversation history

• Long-term memory

• Retrieved information (from vectors, APIs, MCP tools)

• Tool definitions and their responses

• Structured outputs

  
  

The goal isn't to stuff everything in. It's to curate the right things, in the right order, at the right time.

Three techniques worth your attention:

1. Smart source and tool selection: Before any retrieval happens, the model needs to know what's available. Routing to the right source is already a context decision.

2. Compression and ordering: Context windows are finite. Summarizing retrieved content, ranking results by relevance or date, removing noise — these choices compound into better outputs.

3. Long-term memory architecture: In conversational agents, what gets stored and retrieved between sessions matters just as much as what's in the current window.

   
   

Instead of one giant LLM call with everything crammed in, well-designed workflows break complex tasks into focused steps — each with its own lean, optimized context. Less noise. More signal. Better results.

The builders who master this will build AI systems that are faster, cheaper to run, and genuinely more reliable.