Hallucinations, The Non-Deterministic Nature of Generative AI, and How to Create a Semi-Deterministic, Mission-Critical AI Assistant
AI has revolutionized how we interact with information and automate tasks, but it comes with unique challenges, especially in mission-critical contexts. Terms like hallucinations, non-deterministic outputs, and RAG (Retrieval-Augmented Generation) can sound daunting, but understanding them is crucial for building robust AI systems. This article dives into these concepts and explains how embedding dimensions, JSON schemas, and multi-step RAG workflows leveraging mixed models from multiple vendors can create a more controlled, reliable AI - ideal for mission-critical tasks.
Understanding AI Hallucinations and Non-Determinism
Hallucinations in AI refer to outputs that appear coherent but contain information that is completely fabricated or incorrect. For example, an AI might generate a detailed sounding answer that is not based on any factual data. Hallucinations are a direct consequence of the non-deterministic nature of generative models, like GPT-based systems, which use probabilities to predict what words come next. Because these predictions are based on vast but limited training data, the model can sometimes 'make up' information when unsure.
The non-deterministic nature of AI also means that repeated runs on the same prompt can yield different results. Unlike a simple calculator, generative models don't always produce the same output, which can be an issue when reliability and accuracy are essential, such as in Tech Support, Customer Service and anything related to healthcare, financial services, or legal advisory applications.
Using RAG to Minimize Hallucinations
To mitigate hallucinations and improve the reliability of AI responses, we use Retrieval-Augmented Generation (RAG). RAG augments the generative power of AI with fact-checking capabilities by retrieving external information before generating a response. Instead of relying entirely on its training data, a RAG system queries a vector database containing structured information, ensuring the generated text is rooted in fact rather than speculation. This blend of information retrieval and generation drastically reduces hallucinations.
Embedding Dimensions and Increased Accuracy
The foundation of RAG is embeddings, which are numerical representations of text or data. The more dimensions an embedding has, the more nuanced its representation of information becomes. Imagine trying to describe a person using only three words versus fifty - embedding dimensions work similarly. Higher-dimensional embeddings provide more detailed context, allowing the AI to make more accurate inferences about the data. This greater detail helps in retrieving the most relevant information and makes it less likely for AI to hallucinate by confusing similar concepts.
Enforcing Compliance with JSON Schemas
Another approach to make generative AI more predictable and controllable is using strict JSON schemas to enforce structure and compliance in responses. A JSON schema acts as a template, ensuring that the AI outputs match a pre-defined structure. This is particularly important in mission-critical applications, where each response must comply with a strict format to be useful.
By requiring the AI to produce output that adheres to a specific JSON schema, deviations can be easily detected and managed. For example, if the AI provides information in an incorrect structure, subsequent validation step can reject it, making the overall workflow more robust. JSON schemas help in catching non-compliant responses quickly, adding another layer of reliability.
The Myth of the Seed: Why It Doesn't Guarantee Reliability
The concept of a seed in AI generation is often misunderstood. Many believe that modifying a seed will allow them to achieve a substantially different response to the same prompt, as if it could bypass issues like hallucinations. However, with complex generative models, modifying a seed does not guarantee a materially different outcome for the same prompt. The probabilistic nature of these models means that even different seeds often result in outputs that still share the same underlying issues, such as hallucinations. Seeds can provide repeatability for specific runs but cannot be relied upon to significantly alter the behavior of generative AI in all scenarios.
A Proprietary Hybrid RAG Approach for Mission-Critical AI Assistants
To create a semi-deterministic, mission-critical AI assistant, we propose a proprietary hybrid approach involving a multi-step RAG workflow, attended by different models specializing in various tasks.
- Intent Analysis: The workflow begins with a model analyzing user intent. This step categorizes the query to determine the type of information required and the degree of specificity.
- Initial Retrieval Using Embeddings: Based on the identified intent, the assistant uses embeddings and semantic indexing to retrieve relevant documents from a vector database. High-dimensional embeddings ensure that the retrieved documents are highly relevant.
- Splitting Workflow Across Specialized Models: The retrieved information is then processed by different models, each specialized in handling a particular aspect of the query. For instance, one model may be optimized for raking relevancy of reference documents, while another handles generation of the proposed response.
- Issue Detection and Escalation: The assistant automatically detects areas of uncertainty or missing information, escalating these to intervention models from an alternate vendor. This cross-vendor strategy serves as a 'second opinion,' mitigating the biases and potential hallucinations that could arise if a single vendor's model handled everything.
- Final Generation with Quality Control: Finally, the generative model synthesizes the information, and proprietary quality control measures are used to ensure the accuracy and reliability of the response. Automated escalation to an alternate model from a different vendor is employed if ambiguities or uncertainties are detected. This step helps reduce the risk of errors or hallucinations slipping through, without relying solely on JSON schema validation.
Why This Matters
The future of AI isn’t just about making more creative models; it’s about making reliable ones. In industries where mission-critical AI is required, hallucinations can have severe consequences like financial miscalculations, incorrect medical advice, or faulty legal recommendations.
A proprietary hybrid approach to RAG can help mitigate these risks. By splitting workflows into specialized steps and escalating to intervention models when ambiguity is detected, the system becomes more than just a smart assistant - it becomes a trustworthy partner. Combining this with tools like high-dimensional embeddings and JSON schemas ensures that the output is accurate, structured, and reliable, transforming generative AI into a tool suitable for mission-critical applications.
In a landscape where trust in AI is often questioned due to its non-deterministic nature, approaches like these offer a blueprint for creating semi-deterministic AI systems that are both powerful and dependable. They provide a clear path forward for deploying generative AI that isn't just flashy but is also functional, responsible, and robust enough for real-world challenges.
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