Read this article to find out how they can help to make AI Enterprise proof.
With the media’s fixation on Large Language Models (LLMs), and applications like ChatGPT, you would almost think that the use of AI revolves around a minimal set of tools.
However, more than LLM is needed to meet the demands of enterprise readiness. Instead, enterprises must embrace a comprehensive approach incorporating various cutting-edge AI solutions.
🎓Delving beyond the surface, we discover advanced AI solutions such as:
1. Retrieval-Augmented Generation (RAG),
2. Contrastive Retrieval-Augmented Generation (CRAG),
3. Framework for Learning And REtrieval (FLARE),
4. Mixture of Experts (MoE),
5. And robust strategies to mitigate hallucinations.
Let’s explore how these technologies and practices work and contribute to creating enterprise-ready AI solutions.
📢RAG combines the power of retrieval-based techniques with generative models to produce contextually relevant and accurate responses. By retrieving information from vast knowledge bases and generating responses tailored to user queries, RAG enables enterprises to deliver superior customer experiences and drive customer satisfaction.
Let’s have a look🧐 at how different components of RAG work together to enhance AI:
- Input Prompt: This is the initial input provided to the RAG model, which could be a question, a prompt for generating text, or any other form of input.
- Retrieval Component: The input prompt is used to query a retrieval component, which searches a large corpus of text or a knowledge base to retrieve relevant passages or documents. This component employs techniques such as dense passage retrieval (DPR) to efficiently retrieve relevant information.
- Retrieved Information: The retrieval component returns relevant passages or documents from the corpus based on the input prompt. These retrieved passages contain contextual information that can be used to augment the generation process.
- Generation Component: The retrieved information is passed to the generation component, which consists of a generative language model (e.g., GPT) capable of generating coherent and contextually relevant text. The generation component incorporates the retrieved information along with the input prompt to produce the final generated text.
- Generated Text: The output of the generation component is the generated text, which can include answers to questions, summaries of retrieved information, or any other text-based response relevant to the input prompt.
Overall, RAG combines retrieval-based techniques for obtaining relevant information from a large corpus with generative models for producing coherent and contextually appropriate text. This approach allows RAG to generate high-quality responses grounded in factual knowledge and tailored to the input prompt.
📢CRAG extends RAG by incorporating contrastive learning techniques, enabling the model to better distinguish between correct and incorrect responses. By leveraging contrastive learning, CRAG helps enterprises mitigate the risk of generating inaccurate or misleading content, enhancing brand reputation and regulatory compliance.
In the CRAG (Contrastive Retrieval-Augmented Generation) architecture, the model is updated through a process of contrastive learning. Let’s break down how this works:
- Contrastive Learning Discriminator: The CRAG model incorporates a contrastive learning discriminator, which evaluates the generated text along with negative examples to distinguish between correct and incorrect outputs. Negative examples are typically samples of text that the model should not generate or that do not align with the desired output. The discriminator assesses the quality and relevance of the generated text in comparison to the negative examples.
- Feedback Signal: Based on the evaluation from the contrastive learning discriminator, a feedback signal is generated to indicate how well the generated text aligns with the desired output and how it compares to negative examples. This feedback signal provides valuable information on the strengths and weaknesses of the model’s current output.
- Model Update: The parameters of the CRAG model, including those of the retrieval component, generation component, and potentially other components such as the contrastive learning discriminator, are updated based on the feedback signal. The update process typically involves adjusting the model’s parameters through optimization algorithms such as gradient descent to minimize the discrepancy between the generated text and the desired output while maximizing the contrast with negative examples.
- Iterative Improvement: The model update process is iterative, meaning that the model’s parameters are continuously refined over multiple training iterations. With each iteration, the model learns from the feedback provided by the contrastive learning discriminator and gradually improves its ability to generate high-quality text that aligns with the desired output while avoiding undesirable outputs represented by negative examples.
- Enhanced Generative Capabilities: Through this iterative update process guided by contrastive learning, the CRAG model enhances its generative capabilities over time. It learns to produce text that is not only contextually relevant and coherent but also distinguishes itself from incorrect or undesirable outputs.
CRAG leads to improved performance in generating high-quality text responses grounded in factual knowledge and context, ultimately enhancing the overall effectiveness of the AI architecture.
📢Next up is FLARE. FLARE provides a versatile framework for integrating retrieval-based techniques with generative models. The difference to RAG is that it enables enterprises to access and utilize vast amounts of external knowledge effectively, not just its internal vectorised data. By leveraging external data sources and advanced AI capabilities, FLARE empowers enterprises to gain valuable insights, identify emerging trends, and confidently make data-driven decisions.
The BIG difference between RAG en FLARE is the retrieval component. With FLARE the retrieval component searches various external data sources such as knowledge bases, text corpora, or online APIs to retrieve relevant information. This retrieval process aims to gather contextual information that can be used to enhance the generation process.
Overall, FLARE combines retrieval-based techniques for obtaining relevant information from external data sources with generative models for producing contextually appropriate text. This integration allows FLARE to generate high-quality responses grounded in factual knowledge and tailored to the input prompt.
📢Compared to the previous solutions MoE is different. MoE architectures enable enterprises to harness the collective expertise of multiple specialized models to improve prediction accuracy and robustness. By leveraging individual experts’ diverse perspectives and domain expertise, MoE architectures enable enterprises to make more informed decisions and adapt to changing market conditions effectively.
Let’s have a look at how different components of MoE work together to enhance AI:
- Input Data: This is the initial input provided to the MoE model, which could be a feature vector, a sequence of tokens, or any other form of input data.
- Gate Network: The gate network takes the input data and computes gating coefficients for each expert. The gating coefficients computed using the softmax function provide a probabilistic measure of each expert’s contribution to the final output. Experts with higher raw scores or logits will have higher gating coefficients, indicating that their predictions are more influential in shaping the final output. By dynamically adjusting the gating coefficients based on the input data, the MoE architecture can effectively leverage the expertise of its constituent experts and produce accurate and contextually appropriate predictions.
- Expert Networks: There are N expert networks, each specializing in a different aspect of the input data or task. These experts are typically neural networks with different architectures or trained on different subsets of the data. The experts in a MoE can be diverse, including linear models, decision trees, support vector machines, convolutional neural networks (CNNs), recurrent neural networks (RNNs), or any other type of model that is suitable for the specific task at hand.
- Weighted Sum of Outputs: The outputs of the expert networks are combined using the gating coefficients computed by the gate network. This produces a weighted sum of the expert outputs, where each expert’s contribution is weighted by its corresponding gating coefficient.
- Output: The final output of the MoE model is the result of the weighted sum of expert outputs. This output could be a classification label, a probability distribution, a sequence of tokens, or any other form of output depending on the task.
Overall, a Mixture of Experts model combines the predictions of multiple expert networks using learned gating coefficients to produce a final output. This allows the model to leverage the strengths of different experts and adaptively combine their predictions based on the input data.
📢Mitigating Hallucinations: Hallucinations, or erroneous predictions generated by AI models, pose significant risks to enterprise operations and customer trust. To address this challenge, enterprises can implement robust mitigation strategies, such as uncertainty estimation techniques and human-in-the-loop verification. Uncertainty estimation methods provide measures of prediction uncertainty, enabling enterprises to identify and mitigate potentially unreliable predictions. Additionally, human-in-the-loop verification mechanisms empower domain experts to review and validate model outputs, ensuring accuracy and reliability in enterprise AI applications.
📢Prompt Engineering represents a strategic approach to shaping input prompts to elicit accurate and relevant responses from AI models. By crafting well-defined prompts and providing clear guidelines, enterprises can steer AI models towards generating responses that align with company policies, regulatory requirements, and brand standards. Prompt Engineering ensures consistency and compliance across AI-generated content, safeguarding enterprise reputation and fostering trust among customers and stakeholders.
🤖When using AI, use it wisely🧐, and it will greatly benefit📈 your company.
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