What Is a Reasoning Engine?
Go even deeper into the world of LLMs, so you can make the most of your conversational copilot.
Shipra Gupta
Go even deeper into the world of LLMs, so you can make the most of your conversational copilot.
Shipra Gupta
Imagine if AI could automate routine business tasks like drafting emails, generating campaign briefs, building web pages, researching competitors, analysing data and summarising calls. Automating such repetitive tasks can free up an immense amount of valuable human time and effort for more complex and creative activities like business strategy and relationship building.
Automation of such routine business tasks requires simulating human intelligence by making AI function as a reasoning engine. It’s generative AI at another level. In addition to communicating in natural language, AI will also help with problem-solving and decision-making. It will learn from the information provided, evaluate pros and cons, predict outcomes and make logical decisions. Given the technological advancements of recent times, we are at the precipice of such an AI capability and it has many people in the scientific and business community excited.
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A reasoning engine is an AI system that mimics human-like decision-making and problem-solving capabilities based on certain rules, data and logic. There are three types of human reasoning or inference mechanisms reasoning engines tend to emulate:
By now, people all over the world know that large language models (LLMs) are special machine learning models that can generate useful new content from the data they are trained on. In addition to that, the LLMs today also exhibit the ability to understand context, draw logical inferences from data and connect various pieces of information to solve a problem. These characteristics enable an LLM to act as a reasoning engine.
So how does an LLM solve a common business math problem by evaluating information, generating a plan and applying a known set of rules?
Let’s say a coffee shop owner wants to know how many coffees she needs to sell per month to break even. She charges $3.95 per cup, her monthly fixed costs are $2,500 and her variable costs per unit are $1.40.
The LLM applies a known set of math rules to systematically get the answer:
Identify the relevant values.
Calculate the contribution margin per coffee. Contribution margin is the selling price minus the variable cost.
= $3.95 - $1.40 = $2.55
Apply break even formula. Break-even point is the fixed cost divided by the contribution margin.
= $2500/$2.55 = 980.39
Round up to nearest whole number.
Break-even point = 981 coffees
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The popularity of large language models skyrocketed in fall of 2022, but scientists have been deep in the thick of experimenting with these models through various prompts. “Prompting” or prompt engineering, is now a fast-emerging domain in which a carefully crafted set of input instructions (prompts) are sent to the LLM to generate desired results. When we use prompts to generate a logical plan of steps for accomplishing a goal, we also refer to them as “reasoning strategies.” Let’s explore some of the popular reasoning strategies below:
These are just a few of the most promising strategies today. The process of applying these strategies to a real life AI application is an iterative one that entails tweaking and combining various strategies for the most optimal performance.
It is quite exciting to have LLMs function as reasoning engines, but how do you make it useful in the real world? To draw an analogy with humans, if LLMs are like the brain with reasoning, planning and decision-making abilities, we still need our hands and legs in order to take action. Cue the “AI agent” — an AI system that contains both reasoning as well as action-taking abilities. Some of the prevalent terms for action-taking are “tools,” “plug-ins” and “actions.”
There are two kinds of AI agents: fully autonomous and semi-autonomous. Fully autonomous agents can make decisions autonomously without any human intervention and act on them as well. These kinds of agents are in experimental mode currently. Semi-autonomous agents are those agents that involve a “human in the loop” to trigger requests. We are starting to see the adoption of semi-autonomous agents primarily in AI applications like conversational chatbots, including Einstein Copilot, ChatGPT and Duet AI.
An AI agent has four key components:
Einstein Copilot is Salesforce’s advanced AI-powered conversational assistant, which interacts with a company’s employees and customers in natural language. Employees can use it to accomplish a variety of tasks in the flow of work, helping to increase productivity at scale. And consumers can use it to chat with brands and get questions answered immediately, leading to higher satisfaction and loyalty. Einstein Copilot uses LLMs for language skills like comprehension and content generation and also as a reasoning engine to plan for complex tasks, thereby reducing the cognitive load on users.
Here’s how it works:
Visually, this looks like…
Einstein Copilot gives companies the ability to tap into LLMs as reasoning engines. With this tool, companies can use AI to accomplish a number of tasks that weren’t realistic just a few months ago.
In these use cases and many others like these, Einstein Copilot is essentially acting as a semi-autonomous agent, using LLMs as reasoning engines and taking actions to fulfil tasks when prompted by users. This is just the beginning; the next frontier is making Einstein Copilot fully autonomous so that it is not only assistive but proactive and omnipresent. AI holds a thrilling future, but even more exciting are the results of global efficiency sure to come.
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