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Conventional RAG Falls Short with Enterprise Databases

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Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

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Ensure GenAI compliance and governance 

[Read the Whitepaper![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/effective-ai-governance-wp)

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Taking the AI leap from RAG to TAG

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GigaSpaces, IBM & AWS make AI safer

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    * [API Scaling](https://www.gigaspaces.com/use-cases/application-acceleration)
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      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
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# **What is Constitutional AI?**

[More Glossary Terms](https://www.gigaspaces.com/data-terms)

## **What is Constitutional AI?**

Constitutional AI is a method of aligning AI models, particularly large language models (LLMs), with human values by training them to follow a predefined set of ethical principles. These principles form a “constitution” that acts as a rulebook, guiding the model to be helpful, harmless, and honest.

Unlike traditional alignment techniques, constitutional AI doesn’t depend completely on human reviewers to spot bad behavior. Instead, the model learns to critique and correct its own outputs using constitution AI generated feedback. That’s the shift. Less human micromanagement, and more structured self-regulation. A blueprint for scalable, transparent AI alignment.

This constitutional AI concept was developed by researchers at Anthropic, and it underpins models like Claude. Think of it as teaching an AI assistant not just what to say, but how to think about what it says, before it says it.

## **How Constitutional AI Works**

The process unfolds in two phases: supervised learning and reinforcement learning.

### **Supervised Learning Phase**

  
The training begins with a pre-trained [LLM](https://www.gigaspaces.com/data-terms/llm-evaluation). This model is already helpful, but not always harmless. It's then exposed to difficult prompts, ones that tend to elicit toxic or biased replies. When the model responds, it’s encouraged to critique its own output, guided by a randomly chosen principle from the constitution.

This self-critique isn’t guesswork. Beforehand, the model is shown examples of how to assess and revise outputs. This few-shot learning helps the AI understand the task. Once it identifies a harmful reply, it rewrites the answer to comply with the selected principle.

Over time, these improved responses form a dataset that’s used to fine-tune the model. The result is a more helpful model that understands how to reject harmful paths without dodging the topic altogether.

### **Reinforcement Learning Phase**

Next comes reinforcement learning from AI feedback, also known as RLAIF. Two outputs are generated for the same prompt. The model then chooses the better one, again using a constitutional principle to guide its judgment. This choice becomes a preference signal. The model uses these signals to adjust its behavior.

This second phase replaces traditional human feedback loops. Instead of relying on people to rate outputs, the model learns from its own structured assessments. This is where the constitutional [AI](https://www.gigaspaces.com/data-terms/ai-databases) vs RLHF debate comes in. RLHF (Reinforcement Learning from Human Feedback) depends on time-intensive human annotation. Constitutional AI sidesteps this, offering a more scalable (and potentially less biased) alternative.

## **The Benefits of Constitutional AI**

There are several clear benefits of Constitutional AI:

**Transparency and control:** With clearly defined rules, developers and users know what the AI is trying to do and why. It’s not a black box. The constitutional AI concept puts ethical reasoning front and center.

**Scalability:**[ Human feedback](https://www.gigaspaces.com/data-terms/reinforcement-learning-human-feedback) doesn’t scale well. Constitutional AI does. By training models to critique themselves, this approach reduces the reliance on human labelers while improving consistency.

**Better safety:** The AI becomes more sensitive to toxic, biased, or misleading prompts. Instead of evading difficult topics, it learns to [address them responsibly](https://www.gigaspaces.com/blog/responsible-ai).

**Model honesty:** Because the model is trained not just to be nice, but to be accurate, the results are more grounded. It learns to tell the truth, even when the truth is, “I don’t know.”

## **The Applications of Constitutional AI**

Constitutional AI isn’t limited to chatbots. It’s already being used in content moderation, search assistants, summarization tools, and even legal drafting.

Anthropic’s Claude models are the leading example. These systems follow a constitution built from sources like the UN Declaration of Human Rights and various ethical guidelines.

But the method is broader than one model. We’re beginning to see signs of constitutional AI in the LangChain integration, where structured reasoning can help multi-agent systems or retrieval pipelines evaluate their outputs for alignment with human values.

Looking ahead, this framework could shape models in computer vision, robotics, autonomous vehicles, and beyond. From blocking harmful imagery to guiding decision-making in physical environments, the same core idea applies: embed values directly into model training.

## **FAQs**

### **Q: How does Constitutional AI differ from traditional AI alignment?**

Traditional AI alignment relies heavily on human raters, especially through RLHF. These human feedback loops are subjective and slow. Constitutional AI uses rule-based self-assessment instead. The model critiques itself using constitution AI-generated guidelines, making the training faster, cheaper, and often more consistent.

### **Q: Can Constitutional AI eliminate bias in language models?**

It can help. By embedding anti-bias principles into the constitution, the model can be trained to avoid biased responses. That said, no method is perfect. Bias is a product of data, context, and design. Constitutional AI is a powerful tool, but not a magic fix.

### **Q: How is Constitutional AI implemented in practice?**

Implementation typically involves:

  * Choosing a set of guiding principles (the constitution)
  * Fine-tuning a model to critique and revise its responses using those principles
  * Training preference models through AI-generated feedback
  * Using reinforcement learning to refine the base model’s behavior



It’s a blend of supervision and self-regulation. Think of it as giving the AI a code of conduct, —and the skills to follow it.

### **Q: Is Constitutional AI used in open-source models?**

Not widely yet, but interest is growing. Most examples so far come from proprietary systems like Claude. However, as constitutional training methods become better understood, we may see [open-source LLMs](https://www.gigaspaces.com/data-terms/llm-hallucinations) adopting similar techniques. Research groups are already experimenting with public constitutions to bring alignment to the broader developer community.

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