Lecture 14

Implement Retrieval-Augmented Generation (RAG) to Accelerate LLM Application Development

Large language model (LLM) applications, such as chatbots, are unlocking powerful benefits across industries. Organizations use LLMs to reduce operational costs, boost employee productivity, and deliver more-personalized customer experiences.

As organizations like yours race to turn this revolutionary technology into a competitive edge, a significant portion will first need to customize off-the-shelf LLMs to their organization’s data so models can deliver business-specific AI results. However, the cost and time investments required by fine-tuning models can create sizable roadblocks that hold many would-be innovators back.

To overcome these barriers, retrieval-augmented generation (RAG) offers a more cost-effective approach to LLM customization. By enabling you to ground models on your proprietary data without fine-tuning, RAG can help you quickly launch LLM applications tailored to your business or customers. Instead of requiring retraining or fine-tuning, the RAG approach allows you to connect the off-the-shelf LLM to a curated external knowledge base built on your organization’s unique, proprietary data. This knowledge base informs the model’s output with organization-specific context and information.

Large Language Models as Optimizers

Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to our main application in prompt optimization, where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
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When Large Language Model Meets Optimization

Optimization algorithms (OA) is becoming increasingly important as a class of heuristic search algorithms in the broad field of artificial intelligence and machine learning. OA draws on the natural mechanisms of biological evolution, including processes such as natural selection, heredity, mutation, and hybridization, for solving complex optimization problems. These algorithms are widely used in many fields due to their global search capability, low dependence on problem structure, and ease of parallelization. Optimization algorithms pivotal in diverse fields such as logistics, finance [84], healthcare [88], and artificial intelligence [25], aim to identify the best solution from available alternatives. They are essential for making decisions efficiently and effectively in an era of rapidly increasing data complexity and volume. The continuous advancement in optimization techniques has resulted in significant enhancements to algorithmic strategies, each customized to address specific types of problems and operational constraints. From deterministic methods addressing linear problems to stochastic approaches for global optimization under uncertainty, optimization algorithms hold promise across a broad spectrum of research and practical applications. With the development of technology, especially when dealing with large-scale, high-dimensional and dynamically changing optimization problems, traditional algorithms often face performance bottlenecks. Evolutionary computing provides effective solutions to these problems with its unique search strategy. In addition, the flexibility and adaptability of evolutionary computation enable it to be combined with a variety of other computational techniques to form hybrid algorithms for further performance enhancement.
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Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective

This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective optimization. Focusing on the transformative role of Large Language Models (LLMs), our study investigates the potential of LLM-Assisted Inference to automate and enhance decision-making processes. Specifically, we highlight its effectiveness in illuminating key decision variables in evolutionarily optimized solutions while articulating contextual trade-offs. Tailored to address the challenges inherent in inferring complex multi-objective optimization solutions at scale, our approach emphasizes the adaptive nature of LLMs, allowing them to provide nuanced explanations and align their language with diverse stakeholder expertise levels and domain preferences. Empirical studies underscore the practical applicability and impact of LLM-Assisted Inference in real-world decision-making scenarios.
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