How LLMs Think Step by Step & Why AI Reasoning Fails

Why It Matters

Step‑by‑step reasoning transforms LLMs from fast but error‑prone chatbots into reliable decision‑making tools, directly affecting enterprise risk and productivity.

Key Takeaways

• Chain-of-thought prompting forces models to articulate reasoning steps.
• Direct answers often cause logical errors on multi-step queries.
• Reasoning-focused models embed step-by-step thinking intrinsically, improving answer reliability.
• State-of-the-art models like Gemini 2.5, GPT-5, Claude Opus excel.
• Prompt engineering improves accuracy and reduces hallucinations in complex tasks.

Summary

The video explains how large language models (LLMs) often stumble on multi‑step questions because they attempt to jump straight to a final answer, leading to logical slips and hallucinations. To mitigate this, practitioners employ a prompt‑engineering technique called chain‑of‑thought (CoT), which adds a simple instruction such as “let’s think step by step,” forcing the model to lay out its reasoning before concluding.

By explicitly breaking down a problem—defining concepts like Retrieval‑Augmented Generation (RAG), comparing it to fine‑tuning, and then drawing a conclusion—the model’s accuracy improves dramatically on complex tasks. Newer reasoning‑focused models have taken this a step further: they generate internal step‑by‑step thought processes automatically, without needing a special prompt. Examples cited include Google Gemini 2.5 Pro, OpenAI’s GPT‑5, and Anthropic’s Claude Opus, all of which demonstrate markedly better performance on intricate queries.

The presenter highlights a key quote: “let’s think step by step,” illustrating how a tiny prompt tweak can change the entire inference pipeline. He also notes that the latency, token usage, and answer style depend on whether the model follows a CoT chain or a direct answer path, underscoring the engineering trade‑offs behind the scenes.

The implication is clear: embedding structured reasoning—either via prompts or model architecture—reduces errors, curtails hallucinations, and makes LLMs more trustworthy for business-critical applications such as research, compliance, and decision support.

Original Description

Day 15/42: Reasoning & Chain-of-Thought

Yesterday, we learned how examples help.

But some questions still break models.

That’s a reasoning problem.

Chain-of-thought works by forcing intermediate steps:

“Let’s think step by step.”

Instead of jumping to an answer, the model lays out its logic.

That alone can dramatically improve accuracy.

Newer models do this internally.

Older ones need a nudge.

Missed Day 14? Watch it first.

Tomorrow, we look at what happens when you press enter: inference.

I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀

#ChainOfThought #Reasoning #LLM #short