Beginners Guide to Reasoning in AI

If you've been anywhere near LLMs lately, you've probably heard the word "reasoning" thrown around more than a frisbee at a college campus. GPT-4 can "reason" through complex problems. Claude can "reason" about ethical dilemmas. O1 has "reasoning" capabilities that seem almost magical. But what does this actually mean?

Are we witnessing the birth of digital minds that can truly think, or are we just getting really good at teaching computers to mimic human thought patterns? And more importantly, should we be excited or terrified?

DON’T PANIC! We're going to break this down in plain English. No PhD required, no complex equations, just a clear look at what's happening under the hood and why it matters for all of us.

Whether you're in sales trying to understand what we're building, in marketing figuring out how to explain this stuff to customers, an engineer wanting to build reasoning systems, or just here for a good time and some mind-bending AI insights, this series will give you everything you need to stay on top of state-of-the-art LLM models.

Let's start from the beginning and figure out what reasoning actually is, how it works in LLM models, and why everyone's losing their minds over it.

What is Reasoning, Really?

Think about the last time you solved a puzzle, figured out why your car wouldn't start, or decided what to have for dinner. In each case, you weren't just randomly guessing, you were reasoning. You took information, connected it to what you already knew, and followed a logical process to reach a conclusion.

Reasoning is fundamentally about making connections between ideas, drawing conclusions from evidence, and solving problems in a systematic way. It's what separates "I have a hunch" from "I can explain exactly why this makes sense."

But here's where it gets interesting: reasoning isn't just one thing. It's actually a whole toolkit of mental processes that we use depending on the situation.

The Different Types of Reasoning

Logical Reasoning: The Rule Follower

This is the most formal type of reasoning – the kind that follows strict rules and produces definitive answers. Think of it like a very smart calculator that works with ideas instead of numbers.

For example:   All cats are mammals  → Fluffy is a cat → Therefore, Fluffy is a mammal

This type of reasoning is what computers have traditionally been best at. Give them clear rules, and they can follow them perfectly every time. But real life is a little bit trickier than logic textbooks.

Mathematical Reasoning: The Problem Solver

This goes beyond just crunching numbers. It's about understanding relationships, patterns, and using mathematical concepts to solve problems. When you figure out how to split a restaurant bill or calculate whether you can afford that vacation, you're using mathematical reasoning.

The interesting part? It's not just about getting the right answer, it's about understanding the process. A human might solve a math problem by trying different approaches, recognizing patterns, or even making educated guesses and checking their work.

Causal Reasoning: The Detective

This is about understanding cause and effect, figuring out why things happen and predicting what might happen next. It's the difference between knowing that "ice cream sales and drowning incidents both increase in summer" and understanding that hot weather causes both (correlation vs. causation).

Causal reasoning is what you use when you troubleshoot problems, make predictions, or try to understand why something went wrong. It's incredibly powerful but also tricky, humans are notoriously bad at it sometimes.

Analogical Reasoning: The Pattern Matcher

This is perhaps the most human form of reasoning. It's about recognizing that two different situations share important similarities and using that insight to understand or solve problems. When you learn to drive a motorcycle because you already know how to ride a bicycle, you're using analogical reasoning.

This type of reasoning is everywhere in how we communicate. We use metaphors, analogies, and comparisons constantly to explain complex ideas. "The internet is like a highway system" isn't literally true, but it helps us understand how information flows.

How Humans Actually Reason

Here's something that might surprise you: humans are actually pretty bad at formal reasoning. We make logical errors, fall for cognitive biases, and often reach the right conclusions for completely wrong reasons.

But we're incredibly good at practical reasoning, the kind that gets us through daily life. We can:

• Deal with incomplete information
• Make reasonable assumptions
• Recognize when we don't know something
• Combine different types of reasoning fluidly
• Use intuition and experience to guide our thinking

Most importantly, we can explain our reasoning to others. When someone asks "why did you decide that?", we can usually walk them through our thought process, even if we didn't consciously think through every step.

Enter the Machines

For decades, AI researchers tried to build reasoning systems by programming in explicit rules and logical structures. These "symbolic AI" systems were great at formal reasoning but terrible at dealing with the messy, ambiguous real world.

Then came neural networks and machine learning, which took a completely different approach. Instead of programming rules, we trained systems to recognize patterns in data. These systems became incredibly good at tasks like image recognition and language translation, but they were black boxes, we couldn't understand how they made decisions.

The breakthrough came when researchers realized that very large language models – trained on vast amounts of text – seemed to develop reasoning abilities without being explicitly programmed for them.

How LLMs Learn to Reason

So we've established that reasoning is complex and multifaceted, but how do machines actually learn to do it? This is where things get really interesting. Unlike traditional AI systems that were explicitly programmed with reasoning rules, modern LLMs develop reasoning capabilities through training techniques that are both elegant and surprisingly effective.

The breakthrough came when researchers discovered that very large language models, trained on vast amounts of text, seemed to develop reasoning abilities as an emergent property. But getting from "sometimes reasons correctly" to "consistently reasons through complex problems" required some clever innovations.

What we're seeing is an evolution from external techniques (prompting) to internal capabilities (architectural changes). We're moving from systems that can follow reasoning templates to systems that can develop their own reasoning strategies. Whether this represents genuine understanding or sophisticated pattern matching remains an open question, but the practical capabilities are undeniable. The progression thus far has been:

We're moving from systems that can follow reasoning templates to systems that can develop their own reasoning strategies. Whether this represents genuine understanding or sophisticated pattern matching remains an open question, but the practical capabilities are undeniable.

Note: there are other reasoning methods but we will be focusing on these state-of-the-art methods for this series.

Why This Matters (And Why Everyone's Freaking Out)

The implications are staggering. If we can build machines that reason like humans, we're not just talking about better chatbots or more accurate search results. We're talking about AI that can:

• Conduct scientific research
• Solve novel problems creatively
• Make complex decisions with incomplete information
• Explain their reasoning in ways humans can understand, thereby improving trust in AI models

This could revolutionize education, accelerate scientific discovery, and transform how we work and think. But it also raises important questions about safety, control, and what happens when machines become better at reasoning than we are.

What We're Going to Explore

Over the next few posts, we're going to dive deep into this reasonably interesting world. Here's what's coming:

Part 2: How LLMs Learn to Reason - We'll explore the training techniques that give modern AI systems their reasoning abilities. From chain-of-thought prompting to reinforcement learning from human feedback.

Part 3: Building Our Foundation - We'll implement a basic large language model from scratch, giving us a foundation to understand how these systems actually work under the hood.

Part 4: Adding Reasoning Capabilities - The main event! We'll enhance our model with reasoning abilities, showing exactly how machines learn to think through problems step by step. 

Part 5: Lessons Learned and Future Directions - We'll wrap up with insights from our journey, discussing what we've learned about machine reasoning and where this technology might be heading.

Ready to build and reason with the best? Spin up your own LLM training environment on Lambda: no setup, just speed.