Stop Guessing: Build Robust AI with Layered CoT

Key Takeaways

true AI isn't about one giant leap of faith it's built incrementally with every step verified and refined through collaborative effort hi I'm Manish sanwal director of AI at newscorp my work focuses on AI reasoning explainability and automation today I'm excited to show you how we can build AI that just isn't smarter but also more structured and self-correcting using layer Chain of Thought with multi multi- agentic systems let's start with the basics what are multi- agentic systems and simple terms they are collection of specialized AI agents that work together to tackle a complex task each agent is designed to handle a specific part of the overall problem rather than relying on massive monolith systems take self-driving cars for example instead of depending on a massive system picture it as a team of specialized agents one detects pedestrian other reads traffic signal maybe a third one which checks for the best route with each agent doing its part in harmony the entire system becomes much more robust and efficient the modular approach offers several concrete advantages specialization uh each agent can be finely tuned for a specific task leading to a better accurate accuracy and performance since the system is distributed individual agents can be updated or improved without overhauling the entire system so the system becomes flexible and scalable if one agent encounters an issue the other can often compensate ensuring that overall system remains reliable and fall tolerant by integrating these well-coordinated agents we create a system that is inherently more robust and effective and when we add Chain of Thought reasoning into the mix each agent not only performs its task but also explains its decision-making process step by step this com this combination enhances both transparency and resiliency in our AI system so what is Chain of Thought Chain of Thought is a method that guides AI to Think Through the problem step by step rather than simply guessing the answers traditionally when we work with large language models we provide them with the detailed prompt and ask for a final answer even if we Supply extensive context the model often jumps directly to a conclusion without revealing how it arrived there almost as if it's just guessing now imagine if instead of demanding the answer outright we ask the model to walk us through its reasoning process outlining every step along the way this is the essence of Chain of Thought prompting by breaking down a complex problem into a series of manageable step the model not only demonstrate how it processes the information but also exposes the path it takes to reach the conclusion it this approach has two key benefits transparency for one we get to see each stage of reasoning process which helps us understand how the model is tracking the problem second opportunity for fine-tuning and debugging if we spot a mistake in any of the intermediate steps we can adjust the prompt or the process allowing us to correct errors before the final answer answer is provided so in short Chain of Thought transforms the ai's internal reacing into viable and verifiable sequence making the entire process more interpretable and R in summary instead of Simply guessing the AI follows clear logical sequence of steps this approach Chain of Thought makes the AI reasoning process transparent but it comes with several limitations the process is highly sensitive to how the prompts are phrased even a slight change in wording or context can lead to a very different output this means that two almost identical prompts might yield vastly different Chain of Thought complicating both reproducibility and reliability as the AI generates its reasoning step by step there is no builted mechanism to verify or correct mistakes during the process this absence of realtime feedback means that there is no error correction opportunity each step in the chain is produced without continuous validation if an early inference was flawed this can cause a Cascade of errors that compromises the Integrity of the entire process without ongoing checks the model is forced to rely on initial assumptions and the only opportunity to correct is correct it is after the inference is complete when phase with problem that involves multiple interdependent factors Chain of Thought can sometime Miss critical connection the model might not fully integrate all the possible variables into its reasoning resulting in oversimplified or incomplete conclusion in a sense while Chain of Thought provides a transparent step-by-step framework for AI reasoning it's sensitive to prompt design lack of real-time feedback loop and un verified reasoning these are some of the challenges that we try to address it brings us to layer Chain of Thought prompting what I like to call layered coot for short this approach is designed to overcome the limitation of standard Chain of Thought methods by integrating a verification Step at every stage of the reasoning process it works in Two Steps step one generation of initial thought the AI agent Begins by producing an initial thought this is the first piece of reasoning generated from the input prompts at this stage the model formulates an early hypothesis of the problem and it serves as the starting point for the further reasoning step two verification against the knowledge base before moving on the generated thought is immediately verified this involves cross referencing the output against a structured knowledge base or an external database in practice this might include a fact checking algorithm a consistency check through contextual reasoning or maybe a model an emble model to check for the accuracy but this verification step is really crucial it ensures that only accurate and reliable information is allowed to influence subsequent reasoning once the thought is verified the process continues to the next reasoning step this iterative process repeats repeatedly generates a new thought verifies it and then process it the chain of reasoning is thus built up step by step with each link in the chain confirmed before the next Ed the benefit of this additional verification step are significant self correction for one the verification at each step allows the system to catch and correct errors early preventing mistakes from propagating through the entire reasoning chain second dness against prompt variability because each step is independently verified the overall process becomes less sensitive to small changes in the input leading to high reproducibility each verified step ensures that the final output is built on the foundation of accurate and validated information resulting in more trustworthy conclusions breaking down the reasoning into discrete verifiable step makes the AI thought process much more transparent allowing for easier auditing and interpretation in essence layer CH Chain of Thought transforms the AI reasoning into robust iterative Frameworks where every step is checked for accuracy this not only mitigates the inherent weaknesses of traditional Chain of Thought but also leads to more reliable reproducible and interpretable AI models in summary layer Chain of Thought prompting overcomes the limitation of layer traditional coot by adding verification step after each thought it generates this method can be seamlessly implemented using existing llm tools and integrates perfectly within the multi- agentic systems where each specialized agent contribute to a robust robust system overall layered coot enhances both accuracy and reproducibility by ensuring every inference is validated before proceeding remember the future of AI isn't just about building bigger models but it's about creating system that are structured explainable and reliable by prioritizing transparency self corre self-corrections collaboration and validation We Lay the foundation for the true truly trustworthy AI we have a paper published on layer trade of thought prompting the link uh to the archive is below um I'd love to hear your thoughts on it thank you for your time