Prompt Engineering: The Refinement Loop (How to Make an LLM Critique Itself)

We've built bots that are specific (explicit instructions), formatted (structured JSON), and logical (chain-of-thought). But what about quality?

This post is for you if you've ever asked an LLM for a creative idea (a slogan, an email, a blog title) and gotten a "first-draft" answer that's bland, uninspired, or just okay.

LLMs are like an eager intern: their first idea is rarely their best one. Today, we'll build a Creative Assistant and use the Self-Critique & Refinement technique to force the LLM to review and improve its own work.

The problem: the First Draft Bot

Let's ask our bot for a creative task: writing a slogan.

Use Case: "Write a slogan for our new, eco-friendly water bottle."

graph TD
    A["User: 'Write a slogan for my new water bottle.'"] --> B(LLM)
    B --> C["Bot: 'Here's a slogan: 'Drink Water, Save the Planet.''"]
    
    style C fill:#fff8e1,stroke:#f57f17,color:#212121

Why this is bad:

• It's bland.
• It's generic.
• It's not catchy.

The bot did the task, but the quality is low. It gave us its first, uninspired thought.

The solution: build a refinement loop (Self-Critique)

We will force the LLM to have a "second thought". Instead of asking for just the slogan, we will ask for a process:

1. Draft: Generate the first idea.
2. Critique: Analyze that idea against our criteria.
3. Finalize: Create a new idea based on the critique.

This Self-Critique loop forces the LLM to be more deliberative.

The "How": We'll use Goal Decomposition (from our last post) to define this new, multi-step process.

prompt = """
I need a slogan for a new eco-friendly water bottle.

Please follow this exact 3-step process:

1.  **DRAFT:** First, generate one initial slogan.

2.  **CRITIQUE:** Second, critically evaluate your own DRAFT.
    - Is it short and catchy?
    - Does it clearly communicate "eco-friendly"?
    - What is its single biggest weakness?

3.  **FINALIZE:** Third, based on your critique, write a
    final, improved slogan.
"""

Now let's watch the agent "think."

graph TD
    A["User: Critique-Loop Slogan Prompt"] --> B(LLM)
    
    subgraph PROCESS["LLM's Internal Process"]
        direction TB
        B1["1. DRAFT: 'Drink Water, Save the Planet.'"]
        B1 --> B2["2. CRITIQUE: 'The draft is okay, but not catchy. It's too generic. Its weakness is that it's not memorable.'"]
        B2 --> B3["3. FINALIZE: 'Sip Sustainably. Drink Forever.'"]
    end
    
    B --> C["Bot: Provides DRAFT, CRITIQUE identifying weaknesses, and improved FINALIZE slogan"]
    
    style C fill:#e8f5e9,stroke:#388e3c,color:#212121

Observation: This is a night-and-day difference. The Final slogan is dramatically better than the Draft because we forced the LLM to find the flaws in its own first idea.

We've created a system that iterates on its own work.

Think About It: This technique is fantastic for creative tasks. What other "Critique" criteria could you add to the prompt to get even better slogans? (Hint: Think about "Target Audience", "Brand Voice", etc.)

A simpler hybrid: Rephrase and Respond (RaR)

A full "Draft-Critique-Finalize" loop is powerful, but also very long (and thus, more expensive). A simpler, faster version is the Rephrase and Respond (RaR) technique.

You just ask the LLM to rephrase its understanding of the task first. This forces it to "commit" to a plan, which often improves the result.

The "How":

prompt = """
I need a short, exciting slogan for a new video game
about space pirates.

First, rephrase your understanding of this task.

Then, generate 3 slogans.
"""

graph TD
    A["User: RaR Slogan Prompt"] --> B(LLM)
    B --> C["Bot: Rephrases task understanding, then generates 3 high-energy space pirate slogans"]
    
    style C fill:#e8f5e9,stroke:#388e3c,color:#212121

Observation: The "Rephrasing" step forces the LLM to activate the correct concepts ("high-energy", "science fiction", "pirate theme") before it starts writing. This simple "warm-up" leads to much more targeted and creative results.

Challenge for you

1. Use Case: You are building an "Email Writer" bot.

2. The Problem: You ask, Write an email to my boss asking for Friday off. The bot writes a very basic, bland email.

3. Your Task: Design a Self-Critique prompt to fix this. What "Critique" criteria would you add to make the email more professional, persuasive, and polite?

Key takeaways

• First drafts are rarely best: LLMs benefit from iterative refinement, just like human writers
• Self-critique improves quality: Forcing the LLM to evaluate its own work leads to dramatically better outputs
• Refinement loops create iteration: Breaking tasks into Draft-Critique-Finalize steps mimics professional creative processes
• RaR is a lightweight alternative: Rephrase and Respond provides quick quality improvements without full critique loops

---

For more on building production AI systems, check out our AI Bootcamp for Software Engineers.