When ChatGPT arrived like a tornado in November 2022, the tech world stood up and took notice.
Here was an AI capable of engaging in human-like conversations, generating text with remarkable coherence, and understanding context in ways previously unseen. Its Natural Language Processing (NLP) capabilities sparked a surge of interest in artificial intelligence and machine learning.
Businesses began exploring how ChatGPT could enhance customer service, streamline operations, and drive innovation. The arrival of ChatGPT marked a significant milestone in the evolution of AI, highlighting its potential to transform the way we interact with technology.
But there was, and remains, a bit of a problem. It’s known as hallucination.
This occurs when the AI generates information that is not based on reality or factual data, leading to responses that can be misleading or outright incorrect. Despite its advanced capabilities, ChatGPT – and all other large language models (LLMs) such as Google’s Gemini, Microsoft Copilot and Anthropic’s Claude, sometimes produces plausible-sounding but inaccurate or nonsensical answers. (See ChatGPT is bullshit)
They’re also inclined to be very agreeable, often making things up rather than admitting that they don’t know something.
This issue has raised concerns about the reliability and trustworthiness of AI-generated content, particularly in sensitive applications such as healthcare, legal advice, and finance. Developers and researchers continue to work on mitigating these hallucinations through improved training methods and more robust data validation processes.
Addressing this challenge remains critical to ensuring the responsible and effective use of AI technologies like ChatGPT.
Enter an approach known as RAG.
RAG, in the context of Large Language Models (LLMs), stands for Retrieval-Augmented Generation. It is a technique that combines the strengths of information retrieval systems with generative models to improve the accuracy and relevance of the generated text. The three components are:
- Retrieval: An information retrieval component is used to fetch relevant documents or pieces of information based on the user’s query. It involves searching through indexed data, matching the query with stored documents or information, and presenting the results in a ranked order of relevance.
- Augmentation: The retrieved information is then used to augment the input to the generative model. This helps the model generate responses that are more informed and grounded in factual data.
- Generation: A generative model, such as GPT-3 or GPT-4, uses the augmented input to produce a more accurate and contextually relevant output.
RAG allows you to teach and constrain the LLM to specific information, so it can give better answers by looking up facts from a trusted library before it starts talking.
To do this, you need to get your data into a format that the LLM can easily understand and search through quickly, like turning your books into a special kind of list that helps it find the right answers fast.
This process involves the following high-level steps:
- Collecting Data: Gather all the important information and documents you need from sources such as text files, documents, databases and web scraping.
- Cleaning Data: Remove any mistakes, duplicates and irrelevant information to make the data clear and useful.
- Tokenisation: Convert the text into tokens (words, subwords or characters) that the model can process. This step is crucial as it transforms the text into a format that the model can process.
- Creating Embeddings: Convert the cleaned data (tokens) into numerical codes that the AI can understand. This is done using pre-trained models such as BERT, GPT and others.
- Storing in a Vector Database: Save these numerical codes in a special database that allows for quick searching and retrieval.
- Querying and Retrieval: When a question is asked, the system searches the database for the most relevant information to provide accurate answers.
By indexing data in this manner, the RAG-based system can quickly retrieve relevant information based on the context of the input query. For instance, when integrated with a CRM, the system can convert customer interaction histories and service records into vectors, making it easier to fetch and utilise these data points during customer interactions.
RAG helps in addressing the limitations of LLMs, such as a lack of up-to-date knowledge or where it might be missing expertise in a particular domain. By grounding the generative process in retrieved documents, RAG can produce more reliable and fact-based responses. It is particularly useful in applications like question-answering, where the model needs to provide precise and accurate answers based on a vast amount of information.
While RAG helps to minimise the risk of hallucinations (where the model generates plausible but incorrect information) – it doesn’t solve the problem entirely.
And then there was RAR. (Raarrr!)
Retrieval-Augmented Reasoning (RAR), as used by the decision intelligence platform Rainbird, is an advanced approach that builds upon the principles of Retrieval-Augmented Generation (RAG) but with a specific focus on enhancing reasoning capabilities. RAR models can incorporate human expertise by sitting down with your subject matter experts and building their knowledge into the reasoning process by adding it to the knowledge graph.
Retrieval-Augmented Reasoning represents a significant advancement in AI, enabling systems to not only access vast amounts of information but also to reason about it in a meaningful and human-like way. Here’s an overview of how RAR works:
- Retrieval: Similar to RAG, RAR starts with an information retrieval step. It involves fetching relevant pieces of information or documents from your source data (documents, database, etc) based on the input query. This step ensures that the reasoning process has access to accurate and relevant data.
- Reasoning: Instead of directly generating text, RAR incorporates a reasoning engine that uses the retrieved information to perform logical and inferential processes. This reasoning engine can simulate human-like thought processes, making decisions or drawing conclusions based on the information provided.
- Generation: After reasoning through the information, the system generates responses or outputs that are not only contextually accurate but also logically sound. This ensures that the responses are grounded in factual data and follow a coherent line of reasoning.
Key aspects of Retrieval-Augmented Reasoning with Rainbird.ai include:
- Knowledge Representation: Rainbird employs a knowledge graph to represent information in a way that facilitates complex reasoning. This helps in understanding relationships and dependencies between different pieces of information.
- Inference Engine: The core of RAR is the inference engine that can apply rules, make deductions, and simulate various scenarios based on the retrieved data. This engine is designed to mimic human cognitive processes, enhancing the quality of the reasoning. Where the model might not have enough information to make a decision, it is capable of asking questions to get sufficient context.
- Contextual Understanding: By combining retrieval with sophisticated reasoning, RAR systems can provide answers that are not only factually correct but also contextually nuanced. This is particularly useful in domains requiring deep understanding and complex decision-making.
So, to summarise – Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of information retrieval systems with generative language models to improve the accuracy and relevance of the generated text by grounding it in retrieved documents.
Retrieval-Augmented Reasoning (RAR), as used by Rainbird, builds upon this by not only retrieving relevant information but also incorporating a sophisticated reasoning engine that applies logical rules and simulates human cognitive processes.
This makes RAR superior to RAG because it provides not only factually accurate and contextually relevant responses but also well-founded and logically coherent conclusions, making it particularly useful in complex decision-making and problem-solving scenarios.
The core strength of Rainbird’s RAR lies in its ability to provide nuanced and contextually accurate responses, making it ideal for applications in autonomous decision making, decision support, and complex query answering. By augmenting retrieval with sophisticated reasoning, Rainbird goes beyond mere information retrieval, offering a more holistic approach to AI-driven problem-solving.
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Why is RAR so important for trust in AI?
