Implementing the AI Critiquer Model

Building upon the foundation laid in the previous chapter, let's translate the concept of AI self-critique into a practical implementation. The AI Critiquer model is a central component in the supervised learning phase of Constitutional AI. Its purpose is to evaluate an initial response generated by the base language model ($M_{base}$) against the predefined constitution ($\mathcal{K}$) and produce a critique ($C$). This critique serves as the signal for improving the model's behavior.

Formally, we can represent this step as:

$$C = \text{Critiquer}(P, R_{initial}, \mathcal{K})$$

where $P$ is the original prompt, $R_{initial}$ is the initial response from $M_{base}$, and $\mathcal{K}$ is the constitution. The output $C$ is the critique.

Designing the Critiquer Model

Several approaches exist for implementing the critiquer, each with its own set of trade-offs appropriate for different scenarios and resource constraints.

1. Using the Base LLM ($M_{base}$) as the Critiquer: The simplest approach uses the same LLM that generated the initial response. By crafting a specific prompt that includes the constitution, the original prompt, and the response, you instruct the LLM to evaluate its own output according to the constitutional principles.

   • Pros: Requires no additional model training or management. Utilizes the existing capabilities of the LLM.
   • Cons: The model might exhibit biases, potentially being lenient on its own outputs. It could struggle with complex meta-reasoning required for self-critique, especially if the constitution is intricate or contradicts the model's inherent tendencies. Performance heavily depends on prompt engineering.

2. Using a Separate, Powerful LLM: Employing a distinct, potentially more capable LLM (e.g., a larger model or one specifically known for strong reasoning abilities) can act as a more objective critiquer.

   • Pros: Can offer higher quality, more objective critiques. May handle complex constitutions and subtle violations better.
   • Cons: Introduces dependencies on another model (API costs, latency). Still requires careful prompt engineering.

3. Fine-tuning a Dedicated Critiquer Model: For highly specific requirements or large-scale deployments, you might fine-tune an LLM specifically for the critique task. This involves creating a dataset of (response, constitution excerpt, critique) tuples, potentially bootstrapped using human annotators or the prompting methods above.

   • Pros: Can be optimized for speed and accuracy on the specific constitutional framework. May require less complex prompting at inference time.
   • Cons: Requires significant upfront investment in data collection and model training. Adds model maintenance overhead.

For most implementations, starting with method 1 or 2 (prompting an existing LLM) is the most practical approach.

Crafting Effective Critique Prompts

The quality of the generated critique $C$ is highly sensitive to the prompt provided to the critiquer LLM. A well-structured prompt is essential. Important components include:

• Role Definition: Clearly instruct the model to act as a critiquer evaluating against a specific constitution.
• The Constitution ($\mathcal{K}$): Provide the relevant constitutional principles. If the constitution is long, you might include only the most relevant principles based on the prompt $P$ or the response $R_{initial}$, although including the full text is often safer if feasible within context window limits. Ensure principles are clearly delineated (e.g., numbered list).
• The Original Prompt ($P$): Include the prompt that elicited the initial response. This provides context for the expected output.
• The Initial Response ($R_{initial}$): Provide the response generated by $M_{base}$ that needs evaluation.
• Critique Instructions and Format: Specify precisely what the critique should contain.
  • Should it identify specific principles violated?
  • Should it explain why a principle was violated?
  • Should it assess the severity of the violation?
  • Should it be structured (e.g., JSON)? A common format asks the model to find the first principle violated and explain why. If no principles are violated, it should state that clearly.

Example Prompt Template Structure:

You are an AI assistant acting as a critiquer. Your task is to evaluate an AI-generated response based on a provided constitution. Analyze the response in the context of the original prompt and identify if any constitutional principles were violated.

**Constitution:**
<Principle 1: Description...>
<Principle 2: Description...>
...
<Principle N: Description...>

**Original Prompt:**
<Insert original prompt P here>

**AI Response:**
<Insert initial response R_initial here>

**Critique Instructions:**
Review the AI Response. Identify the single most relevant constitutional principle that is violated by the response. If multiple principles are violated, choose the most significant one. If no principles are violated, state "No violation found." Explain your reasoning for identifying the violation or confirming compliance. Output your critique structured as follows:

Critique:
<State the violated principle number and name, OR "No violation found.">
Reasoning:
<Explain why the response violates the principle, or why it complies with all principles.>

Enhancing Critique Quality with Prompting Techniques

• Few-Shot Prompting: Including 1-3 examples of high-quality critiques within the prompt can significantly guide the critiquer model towards the desired output style and reasoning process. Each example should include a sample prompt, response, and the corresponding critique.
• Chain-of-Thought (CoT) Prompting: Encourage more rigorous analysis by instructing the critiquer to "think step-by-step" before providing the final critique. Modify the prompt to ask the model to first outline its reasoning process (e.g., "Consider Principle 1: Analysis... Consider Principle 2: Analysis... Conclusion...") before outputting the structured critique. This often improves the accuracy of identifying subtle violations.

Handling Complex Constitutions

Constitutions can be lengthy and contain numerous principles.

• Relevance Filtering (Advanced): For very long constitutions exceeding context limits, you might implement a preliminary step to identify potentially relevant principles based on keywords in the prompt or response before constructing the final critique prompt. However, this adds complexity and risks missing relevant principles.
• Hierarchical Constitutions: If the constitution has a structure (e.g., sections, overarching themes), leverage this structure in the prompt or use it to guide relevance filtering.

Implementation Details

Consider the practical aspects of deploying the critiquer:

• API vs. Local Models: Decide whether to use external model APIs (like Anthropic's or OpenAI's) or host your own open-source model. Factor in cost, latency, data privacy, and required computational resources.
• Error Handling: Implement error handling. What happens if the critiquer LLM fails to respond, returns a poorly formatted critique, or hallucinates? Consider retry logic with slight prompt variations or fallbacks (e.g., flagging the response for manual review).
• Batching: If processing many responses, batch requests to the critiquer model's API or inference endpoint to improve throughput and potentially reduce costs. Ensure your error handling can manage partial batch failures.

Example Code Snippet (Python Pseudocode)

Here's a function illustrating how you might structure the call to an LLM API for critique generation:

import hypothetical_llm_api # Assume this library exists

def generate_critique(constitution_text: str,
                      original_prompt: str,
                      initial_response: str,
                      critiquer_model_id: str) -> dict:
    """
    Generates a critique for a given response using an LLM.

    Args:
        constitution_text: The full text of the constitution.
        original_prompt: The prompt that generated the initial response.
        initial_response: The response to be critiqued.
        critiquer_model_id: Identifier for the LLM to use as critiquer.

    Returns:
        A dictionary containing the critique and reasoning, or an error.
    """

    # Construct the detailed prompt using a template
    prompt_template = f"""
You are an AI assistant acting as a critiquer... (rest of template as above)

**Constitution:**
{constitution_text}

**Original Prompt:**
{original_prompt}

**AI Response:**
{initial_response}

**Critique Instructions:**
Review the AI Response... (rest of instructions as above)
"""

    try:
        # Make the API call to the LLM
        response = hypothetical_llm_api.generate(
            model=critiquer_model_id,
            prompt=prompt_template,
            max_tokens=500, # Adjust as needed
            temperature=0.2 # Lower temperature for more deterministic critique
        )

        # Basic parsing (actual parsing depends on LLM output format)
        # This needs strong implementation based on expected output structure
        critique_output = response.text
        # Example: Find "Critique:" and "Reasoning:" lines
        # critique_data = parse_structured_critique(critique_output) # Implement this parser
        critique_data = {"raw_output": critique_output} # Placeholder

        return {"status": "success", "critique": critique_data}

    except Exception as e:
        print(f"Error generating critique: {e}")
        return {"status": "error", "message": str(e)}

# Example usage:
# constitution = load_constitution("path/to/constitution.txt")
# critique_result = generate_critique(constitution, "User prompt...", "Model response...", "claude-3-opus-20240229")
# print(critique_result)

This implementation requires careful prompt engineering, potentially iterative refinement based on observed outputs, and parsing of the critiquer model's response to extract the structured critique information needed for the next stage: generating revisions. The quality of this critiquer step directly impacts the effectiveness of the entire CAI supervised fine-tuning process.