Prompt Engineering vs Loop Engineering: Prompts and Feedback

Prompt engineering is the practice of writing instructions that produce good output from an AI model. Loop engineering is the practice of designing the cycle of tool calls, observations, and decisions that an agent runs when it has to complete a multi-step task. Both matter; they solve different problems.

The simplest way to understand the difference: a well-engineered prompt gets one good answer. A well-engineered loop gets a reliable sequence of actions. Neither skill replaces the other. A good loop still needs clear prompts at each step, and a good prompt cannot compensate for a broken feedback structure.

Tools like fenbs keep the task state, decisions, and lessons between loop runs, so the work survives when a run ends or the agent restarts.

What prompt engineering does

Prompt engineering is about the instruction itself: what words, format, context, and examples make a model produce the output you want. A better prompt reduces ambiguity, gives the model the context it needs, and specifies the format of the answer. It is a craft applied to a single request or to the system prompt that governs a conversation.

Classic prompt engineering techniques include few-shot examples (showing the model examples of good answers), role framing (describing the model's task and audience), chain-of-thought guidance (asking the model to reason before answering), and output format specification. Each technique makes the single call more likely to produce what you want.

See prompt engineering for the full guide, and system prompt examples for concrete patterns.

What loop engineering does

Loop engineering is about the structure that wraps repeated model calls. An agent that writes a report, searches for sources, checks the result, and revises the draft is running a loop. Loop engineering decides: what tools are available, how the agent decides which tool to call, how the result of each call feeds into the next prompt, and when the loop stops.

The term "loop" refers to the observe-decide-act cycle: the agent reads its current state or tool output (observe), decides what to do next (decide), calls a tool or produces text (act), and repeats. A well-designed loop handles tool failures gracefully, avoids running forever, and records what it learned so the next run can start from a known state.

Where they overlap

They overlap at the decision prompt: the instruction that tells the agent what to do next given the current state. That prompt benefits from prompt engineering (clear framing, good examples) and it also needs to account for the loop structure (what tools are available, what state the agent is in, what it should do when a tool fails).

Context engineering — supplying the right information to the model at the right step — connects both. See context engineering vs prompt engineering for more on that relationship, and prompt engineering vs RAG vs fine-tuning for where the other approaches fit.

How to improve a loop

Start by making the loop's state explicit. What does the agent know at each step? What has it done? What is left? A loop that cannot answer those questions cannot make consistent decisions. Durable state storage — a file, a tool call, an external record — lets the agent check its position before deciding what to do next.

• Define a stopping condition before the loop starts. What output or state means it is done?
• Handle every tool failure explicitly. The agent should record the failure and decide what to do, not silently retry or skip.
• Limit the number of iterations. A loop with no ceiling can run until it is cut off, wasting resources and producing partial results.
• Log what each iteration did. The log is the evidence that the loop worked and the starting point for the next run.
• Separate the decision prompt from the action. The prompt that decides what to do next should be distinct from the prompt that does it.

How to improve a prompt inside a loop

Every prompt inside a loop benefits from the same disciplines as a standalone prompt, plus a few extras. The prompt should say what state the agent is in, what it has already done, and what it must not do again. This prevents the model from suggesting an action it already tried.

• Include the current state in the prompt: what was completed, what failed, what remains.
• List the available tools and what each one does, even if the tool schema is passed separately.
• Specify the format of the decision the prompt should produce: a tool name and its arguments, a yes/no, a next step.
• Give an example of a good decision and a bad one. Ambiguous decisions are where loops go wrong.

For a concrete implementation example, see context engineering in Cursor.

When to invest in each

If your AI use case is a single request and a single response — a draft, a summary, a code suggestion — invest in prompt engineering. The loop structure does not apply because there is only one call.

If your use case involves multiple steps, tool calls, or decisions that depend on earlier results, invest in loop engineering. A well-structured loop with mediocre prompts will usually outperform a brilliant prompt in a broken loop.

For most serious agent work, you need both. Write good prompts for each step, and design the loop so those steps connect reliably.

How to do this with fenbs

1. Create a free account and add a project for the agent you are building.
2. Write the loop design as a task plan: the stopping condition, the tools, the state it carries, and the failure handling.
3. Record each design decision in Decisions and rules: why this stopping condition, why this tool, what the agent must not do.
4. When a run fails or produces a wrong result, record the lesson: what went wrong and what to change. The next run starts from a corrected design.
5. Connect your coding assistant to fenbs over MCP. It reads the rules and the lessons before the next build session.