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Welcome back to Inspire AI, the podcast where we explore the cutting edge of artificial intelligence in an accessible way. I'm your host, jason McGinty from AI Ready RVA, and today we're diving into two game-changing concepts in AI Retrieval, augmented Generation RAG for short and REACT short for Reasoning and Acting. These might sound like technical buzzwords, but stick with me. They are revolutionizing how AI systems work, making them more knowledgeable, reliable and even action-oriented. Knowledgeable, reliable and even action-oriented. Imagine an AI that can look up facts on the fly and take actions to solve problems. That's what RAG and React are all about, and in this episode, we'll break down what these technologies are in plain language, highlighting the real-world applications, from smarter customer support to advanced search engines and autonomous agents, and discuss how they're shaping the future of AI. We'll also touch on some related innovations like Toolformer, autogpt, landgraph and DSPy, comparing them to RAG and React. Whether you're a casual listener curious about where AI is headed, or a tech professional looking for deeper insight, we've got you covered with an educational yet engaging exploration. So let's get started. All right, let's start with retrieval augmented generation RAG for short. Consider the court system where you have a judge and a court clerk. The AI model is like the judge, gains knowledge by sending the court clerk to fetch relevant information from an external library, which it then uses to produce a more informed answer. Retrieval Augmented Generation, or RAG, is essentially about giving AI access to the external knowledge source so it can ground its answers in real facts. In simple terms, a RAG system combines a language model like GPT-401, whatever right, that's the part of the AI that generates text With a retrieval system, the part that fetches facts or documents. This means that, instead of relying only on whatever the AI model memorized during training, it can search a library or database on the fly to get the latest and the most important, most relevant and more detailed information it needs. One researcher even likened it to the difference between a closed book exam and an open book exam, where the AI can look up answers in a book. Naturally, an AI with an open book exam where the AI can look up answers in a book Naturally an AI with an open book is going to be more accurate on specific, up-to-date questions, right? So the term RAG was coined in 2020 from a research paper out of Meta AI, and today it's a growing family of methods adopted across the industry.

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It solves several problems If you've used chatbots, like earlier versions of ChatGPT, you probably noticed two big issues Out-of-date knowledge and hallucinations. Rag directly addresses both. Raag directly addresses both by pulling in information from a relevant source at query time. It ensures the model has access to the most current, reliable facts instead of being stuck with only what it learned last year as an example. Also, because the AI can cite or show the retrieved evidence, you, as the user, can verify the answer against sources, which helps build more trust right. In short, rag augments a generative AI model with a real-time knowledge lookup, dramatically improving the accuracy and relevancy of its responses. So, crucially, rag helps prevent the AI from guessing when it doesn't know something. One Stack Overflow article put it quite nicely Every language model has a cutoff to its training knowledge and tends to confidently improvise when asked about facts it hasn't seen. Rag introduces a retrieval step to fill those gaps with real data.

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If you've ever chatted with a bot that knew about yesterday's news or could discuss a document you provided, chances are you were interacting with a RAG-powered system. For example, openai's ChatGPT can use a browsing plugin to fetch current information. That's RAG in action. So, similarly, enterprise chatbots can be connected to company databases so that when you ask what's our travel reimbursement policy or anything like that, the bot actually looks up the policy document and gives the answer grounded in that text. No more making up things, it quotes the manual directly.

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Real world applications of RAG are burgeoning across many fields. Essentially, any scenario where up-to-date or specialized information is needed is a good fit. Think about customer support, a prime example. Companies are using RAG-based assistance to handle support queries by retrieving answers from product guides or internal wikis, so customers can get accurate, sighted responses instead of generic guesses or even having people spend a lot of extra time on digging through the resources themselves. And in the medical field, a doctor could query an AI that's augmented with medical index or journal database. It would retrieve the latest research and patient guidelines to inform its answer. Likewise, financial analysts can ask an AI assistant linked to live market data for the newest trends or reports. And here's another everyday opportunity for RAG search engines. Clearly, the new wave of search assistants like Bing or others use RAG to provide direct answers with references, effectively turning web search into a conversation backed by real-time retrieval. In fact, rag is so useful that major tech players like IBM, microsoft, google, aws, nvidia are all integrating it into their AI offerings. Ibm's own Watson platform includes RAG to keep enterprise AI answers up to date without constant retraining.

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The beauty of RAG is that it's modular. You can plug any knowledge source into an AI model, whether it's your personal notes, a corporate database or the entire internet, the AI can consult that source when answering. Think about it. This opens doors to AI that would behave like an expert assistant in virtually any domain, as long as you give it the right knowledge base, of course.

Speaker 1: 7:50

Now we're gonna dig into react, aka reasoning plus acting. It's a step-by-step approach for AI to think and act. So let's start with what is it? Think about an LLM powered autonomous agent. Such an agent uses planning module to break down tasks, integrates with external tools or APIs to act on the world or fetch info. It maintains memory of past steps. React style prompting allows the AI to alternate between thought, which is your reasoning, and natural language, and action, or calling a tool right, then observing the result before the next thought.

Speaker 1: 8:45

React, short for reasoning and acting, is a paradigm that enables AI models, especially large language models, to not only reason through problems, but also take actions to reach a solution In simpler terms, instead of an AI just spitting out an answer in one go. A React-based AI will think out loud, step by step, and at certain points it can perform an action, such as calling an external tool or running a calculation, maybe even searching for information, and then use the result of that action to inform its next step. This approach is inspired by how humans solve complex problems. For instance, we might break down a problem into its subtasks, do some research or calculations in between and gradually work towards the answer. React teaches AI to do the same by interleaving reasoning steps, or the thoughts, with action steps, where it takes action. So let's break that down with an example.

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Suppose you ask a React-enabled AI how many prime numbers are there between 2,500 and 3,000? A regular AI might try to solve this one in one shot and potentially make an error or guess. A React agent, however, would start by reasoning. It would say to itself first, I should find all the primes in that range. I have a tool to do math calculations, so let me use that. That's the thought. Then it issues an action. For instance, call a prime finding tool or script for that range. It gets back the list of primes, so it's making an observation and then reasons again Now I should count how many primes are in the list. It may then use the calculator tool again or simply count in reasoning and finally give you the answer with the correct count. Throughout this process, the AI is effectively figuring out the steps, not unlike how a human would, combining knowledge, reasoning and tool use. That's the essence of React the AI reasons about what, observes the result and then continues reasoning A loop of thought and action. So why is this a big deal? Because it significantly extends what AI can do.

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Traditional LLMs, if they don't know a fact or can't compute something internally, are stuck, but a React agent can recognize these moments and do something about it. It can look up the needed fact, like RAG, or do a calculation, or even interact with a simulated environment. In technical terms, react turns a language model into an agent that can interact with the world beyond just chat. As one Google research blog put it. This paradigm allows language models to handle not only reasoning tasks, which help figuring things out, but decision-making tasks, where it's actively choosing actions, yielding better performance than doing either one alone. In fact, experiments have shown that React prompting can outperform models that only do chain-of-thought reasoning or only act without reasoning, that only do chain-of-thought reasoning or only act without reasoning by having the model generate explicit reasoning traces. Side note, chain of thought.

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Prompting is a prompt engineering technique that aims to improve language models performance on tasks requiring logic, calculation and decision making by structuring the input prompt in a way that mimics human reasoning. So, as a user, you might say describe your reasoning in steps or explain your answer step by step. You might append that to your query at the end, just before inferencing the large language model. In essence, this prompting technique asks the LLM to not only generate a result but also detail the series of intermittent steps that led to that answer. But anyway, by having the model generate explicit reasoning traces, or thoughts and explicit actions, we also get a side benefit interpretability. We can follow the model's chain of thought and see which actions it took, which is much more transparent than a single black box answer. This makes it easier to diagnose errors and increases trust, since the process is somewhat auditable, right, many of us in highly regulated industries really like this.

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So some real-world applications of React often overlap with what people call LLM agents or AI agents. Essentially, anytime you hear about an AI that can use tools or autonomously perform multi-step tasks, there's likely a react approach under the hood. For instance, consider advanced customer support bots that not only retrieve information using rag, but can also ask the user clarifying questions and update a database record. These bots are doing reasoning, deciding to ask a follow-up and acting making the database update. Another example is in AI coding assistance. A tool like OpenAI's Code Interpreter plugin allowed ChatGPT to execute Python code. A React-based coding assistant could take a user's request, then internally write and run code action to get an answer, like generating a plot or checking a condition, then return the result. This is exactly how some coding assistants debug and verify their outputs.

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Perhaps the most buzzed about use of React is in the realm of autonomous AI agents that can carry out high-level tasks. There's a recent example AutoGPT, which emerged in 2023 as an experimental open source project where an AI agent tries to fulfill a broad goal by breaking it into steps and chaining many reasoning action cycles together. For example, if you tell AutoGPT, help me grow my podcast audience, it can generate a plan, identify tasks like improve website SEO, find social media strategies, then proceed to execute those tasks by Googling information, writing content or calling APIs, iterating until it runs out of ideas or reaches the goal. Autogpt garnered a lot of attention as a glimpse of what fully autonomous AI workers might look like, even though, in practice, it often got confused or stuck. It's a reminder that the tech is still very early. It's a reminder that the tech is still very early. Nonetheless, it showed the world the potential of React-style agents, which are agents that can iterate, experiment and operate somewhat independently to solve complex problems. Companies are now exploring such agents for things like workflow automation. Such agents for things like workflow automation. Imagine an AI agent handling an entire expense report process, from receiving a receipt, extracting data, entering it into the system and sending a reimbursement request all by itself. Nvidia describes LLM-based agents as ideal for tasks like smart chatbots, automated code generation and process automation, precisely because they can use tools and plan actions, not just chat, from scheduling meetings by talking to your calendar to controlling IoT. That's the Internet of Things. Devices with voice commands where the AI decides which device API to call. React is enabling a new wave of AI that doesn't just answer questions but gets things done.

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Now I want to talk about some emerging technologies and similar approaches. As RAG and React have risen to prominence, a host of similar or complementary technologies have emerged. These aim to push the boundaries of what AI can do by extending the idea of tool use and reasoning. Let's look at a few notable ones and how they compare. Toolformer from Meta, a method that literally teaches a language model to use tools by itself. Researchers at Meta found a way to train an LLM to decide which APIs to call, then to call them, and how to incorporate the results into its answer, all in a self-supervised fashion. The motivation is similar to RAG and React Base LLMs are great with language, but struggle with things like arithmetic or up-to-date facts, which simpler tools can handle easily. Tool Farmer bridges that gap, achieving the best of both worlds by letting the model offload certain tasks to external tools like a calculator or a search engine translator, and then merge the results back into the response. In essence, it's a trained version of what React does, without prompting. The model learns when and how to act. The result was improved performance on many tasks, matching larger models' abilities by using tools, all without sacrificing the language model's original skills.

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I spoke about AutoGPT before. It's an open-source project that became the poster child for autonomous AI agents. Autogpt is built on OpenAI's GPT-4 and was designed to automate multi-step projects and complex workflows with minimal human input. You give it a high-level goal and it will break that goal into subtasks, prioritize them and tackle them one by one, creating something like a to-do list for itself. Under the hood, autogpt enables a loop of reasoning and acting very much in line with React principles. It can use plugins to access the internet or other apps, could self-chain its outputs to continuously work on a problem. While AutoGPT often struggled with consistency and sometimes went in circles a common challenge for unrestrained AI agents it sparked a huge interest in agentic AI. It's basically a showcase of React-style prompting taken to the extreme, where the AI creates its own plan and tools usage to meet a broad objective. This concept has led to many spinoffs and inspired frameworks for building your own agents. In fact, after AutoGPT went viral, we saw a surge of projects and research into making such agents more reliable.

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Next is Langraph. So this is a newer framework from makers of Langchain which helps design and manage complex AI agent workflows. If React is about how an agent thinks and acts stepwise, langraph is about structuring the overall process. It uses a graph-based architecture to lay out an AI workflow as a network of nodes and edges, to lay out an AI workflow as a network of nodes and edges, where each node could be a step or an action and the edges define the flow or decision paths In plain language. Laingraph lets developers create a map of an AI agent's tasks. You can specify branches, loops, dependencies between subtasks and attach language model reasoning or tools at each node. This makes it easier to build agents that have to handle complex decision trees or multi-step processes in a robust way. The graph approach brings transparency and modularity, so one can monitor the agent state at each node, kind of like checking its notebook as it works, and adjust the workflow by tweaking the graph structure. Langraph isn't a competing idea to RAG and React, but rather an orchestration tool. It can incorporate RAG and React, but rather an orchestration tool. It can incorporate RAG for knowledge lookup or React-style nodes for tool use, all within a controlled graph. Think of it as giving the developer a higher level control panel to visualize and manage an AI agent's reasoning paths, which is especially useful for enterprise settings where reliability and traceability are key.

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Finally, we have DSPy, standing for declarative self-improving Python. Dspy is an open source framework from Stanford that tackles AI development from another angle. While RAG and React are focused on the AI's capabilities, dspy focuses on the developer experience of building AI systems. Dspy focuses on the developer experience of building AI systems. It allows you to program LLM behaviors using code modules instead of hard-to-maintain prompt scripts. In current LLM applications, a lot of effort goes into writing prompts and chaining models together, often using frameworks like Langchain. This can lead to what some call prompt spaghetti Complex, brittle logic scattered across prompts. Dspy's solution is to let developers write modular Python code that declares what the AI should do, for example, define a retrieval step and then reasoning step, then a tool call, etc. Etc. And the framework handles translating that into optimized prompts. Under the hood, it's like moving from assembly language to a high-level language.

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You describe the AI pipeline at a high level and DSPy worries about the prompt engineering details. So in context of our discussion, dspy can be seen as a way to implement things like RAG or React more robustly. Instead of manually rewriting a prompt with a dozen examples, a developer could use DSPy to compose a reasoning module and a tool-using module and let the framework figure out the optimal prompting. This trend reflects a maturing industry. As techniques like RAG and React become essential, tools like DSPy are emerging to make building production-quality AI systems easier and less error-prone. All of these technologies Toolformer, autogpt, landgraf, dspy and others are interconnected in the sense that they're advancing the idea of AI that is both knowledgeable and action-capable. They build upon the foundations of RAG and React. For instance, transformer and AutoGPT are direct descendants of the React philosophy, while Landgraf and DSPy are part of the ecosystem enabling and managing those advanced abilities in real-world applications.

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The key takeaway is that the AI community is actively tackling the limitations of vanilla AI models by connecting them with tools, knowledge bases and structured reasoning strategies. It's a very exciting time, with research and practice informing each other. New ideas like React spawn new tools like these talked about, which in turn, make it easier to deploy even more sophisticated AI assistance. So as we look ahead, it's clear that technologies like RAG and React are shaping the future of AI in profound ways.

Speaker 1: 27:13

Not long ago, we thought of AI assistance as either all-knowing or completely useless. If a question fell outside their training data, you got a blank stare or a wrong answer, and if a task required multiple steps, the AI simply couldn't handle it in one go. Now, with retrieval augmentation, ai systems can continually learn and stay current without needing to be retrained from scratch. They can also provide evidence for their answers, like links and excerpts, which is crucial for trust. Meanwhile, the React paradigm and the rise of AI agents show a path forward toward AI that can engage with the world, not just generate text. This means future AI could schedule your appointments, do your shopping online, troubleshoot your software or even conduct research for scouring databases, all through a natural conversation with you where the AI transparently walks through the steps.

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Industry trends already point to this direction. As mentioned, all the big cloud providers and AI companies are incorporating RAG to make their models more reliable and enterprise ready. Some have called retrieval augmentation and tools like it the future of generative AI, because it addresses fundamental weaknesses, like hallucinations, that have so far limited to the adoption of AI in high-stakes areas. Similarly, there's enormous momentum behind the idea of AI agents. Countless startups and research labs are working on improving autonomous AI, decision-making, planning and tool use. Since the splash of AutoGPT, we've seen better frameworks, evaluations and even governance approaches for agentech AI.

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We should expect upcoming AI systems whether it's the next Siri, alexa or a business automation tool to leverage these capabilities. They won't operate as a monolithic black box. They shouldn't anyway. Instead, they'll be hybrids Part knowledge retriever, part reasoner, part executor. This hybrid approach is how we get closer to AI that behaves intelligently in a human-like sense it can recall facts when needed, break down problems, use instruments to get things done and explain its thought process For the general public. What this means is more useful and trustworthy AI in everyday life. Imagine customer service bots that actually solve your issue, because they can look up your account information and company policies, thanks to RAG, and perform actions like issuing a refund, thanks to React, all in one interaction. Or even personal assistants that don't just set reminders but can handle complex chores like planning a trip by researching destinations, comparing prices and even booking the tickets.

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For you and for the technical folks and AI professionals listening, the message is that the tool-using, reasoning AI paradigm is here to stay and it's evolving fast. With frameworks like LANGRAPH and DSPy, plus ongoing improvements and prompting strategies, it's becoming easier to build sophisticated AI systems that were practically impossible just a couple of years ago. Of course, there are challenges to iron out. React-based agents need to be made more robust and reliable. Ensuring safety is also paramount. When you let an AI act autonomously, you need guardrails so it doesn't do something unintended. Likewise, rag systems are only as good as the data sources they have, curating and updating those knowledge bases will be an ongoing task.

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Nonetheless, the trajectory is set. We are moving from a world where using AI meant phrasing a question and hoping the singular model in the cloud knows the answer to a world where using AI means engaging a dynamic problem solver. This AI will fetch information, perform intermediate computations and interact with various services to help you, much like a human assistant would, but at digital speed and scale. Retrieval, augmented generation and React are two pillars of this new generation of AI. One makes the AI knowledgeable and up to date. The other makes it active and process driven. Together they are enabling AI systems that can truly assist and not just inform. To close us out, it is an inspiring time in AI For anyone worried that AI was just a fancy autocomplete that sometimes fibs, rag and React show that we are actively in engineering our way past those limitations.

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By grounding AI in real knowledge and giving it the ability to reason and act, we are turning these models into something much, much more powerful and useful. So the next time you use AI-powered app and it cites a source or completes a complicated task for you, you'll know a bit about the clever techniques behind the scenes that made it possible. So thank you for tuning in to this episode of Inspire AI. We hope you learned something new about retrieval, augmented generation, react and the future of AI agents. Until next time, stay curious, stay informed and keep innovating.