When we start integrating AI tools into our daily development workflow, we often think it's all about writing better prompts. But as we dig deeper, we realize Prompt Engineering is only layer one.
Today I learned that understanding how modern AI tools work comes down to four distinct layers:
1. Prompt Engineering
What I tell the model.
This is the foundation: crafting clear system instructions, setting explicit boundaries, formatting outputs, and providing few-shot examples. It governs the immediate reasoning and output of the model.
2. Context Engineering
What the model "knows" at each step.
LLMs are only as good as the information in their context window. Context engineering is the art of dynamically injecting relevant data into the prompt—via Retrieval-Augmented Generation (RAG), search outputs, memory stores, or trimmed conversation histories—without exceeding token limits or causing "lost in the middle" phenomena.
3. Harness Engineering
The environment, mechanisms, and controls with which the agent operates.
The harness (or scaffold) is the runtime environment that wraps the LLM. It manages API retries, handles tool integration (calling functions or web tools), enforces structured output schemas, and manages fallback behavior if the model hallucinates or fails.
4. Loop Engineering
How the system self-corrects and iterates.
Loop engineering moves past one-shot answers by putting the LLM inside an iterative feedback loop. It gives the model the ability to run trial-and-error workflows—evaluating its own work, using tool feedback, and revising its steps until it meets the final goal.
The Takeaway
| Layer | Focus Area |
|---|---|
| Prompt | Instructions, examples, format |
| Context | RAG, memory, conversation history, injected state |
| Harness | Tool routing, permissions, validations, safety rails |
| Loop | Multi-turn reasoning, self-correction, agentic workflows |
Even if we're not developing AI systems ourselves, knowing these layers helps us work more effectively with AI-assisted development tools—we'll better understand their capabilities, limitations, and how to get the best results from them.