Topic
Reasoning
Learn Reasoning as a connected topic across chapters, concepts, simulations, and interview reasoning.
10 Concepts5 Articles1h 52m
Overview
Learn Reasoning as a connected topic across chapters, concepts, simulations, and interview reasoning.
How this topic helps
Llm
Ai
Ai Agents
Langchain
Learning Path in this Topic
Series that contain articles from Reasoning. Select a path to filter the article list.
Articles
5 matched articles
Article 1Chain of Thought Prompting: Teaching LLMs to Think Step by StepTLDR: Chain of Thought (CoT) prompting tells a language model to reason out loud before answering. By generating intermediate steps, the model steers itself toward correct conclusions — turning guessw27 min
Article 2RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)📌 TL;DR Summary
Use RAG when facts change frequently and answers must be source-grounded.
Use fine-tuning when you need stable behavior: tone, format, and domain-specific reasoning.
Use RAG + fine-t31 minArticle 3LLM Observability: Tracing, Logging, and Debugging Production AI SystemsTLDR: 🔍 LLM observability is radically different from traditional APM—non-deterministic outputs, variable token costs, and multi-step reasoning chains require specialized tracing. LangSmith provides 19 minArticle 4LangChain Tools and Agents: The Classic Agent Loop🎯 Quick TLDR: The Classic Agent Loop
TLDR: LangChain's @tool decorator plus AgentExecutor give you a working tool-calling agent in about 30 lines of Python. The ReAct loop — Thought → Action → Obser21 min
Article 5Large Language Models (LLMs): The Generative AI RevolutionTLDR: Large Language Models predict the next token, one at a time, using a Transformer architecture trained on billions of words. At scale, this simple objective produces emergent reasoning, coding, a14 min