· SuperML.dev · agenticAI
LangChain Output Parsers: Structuring LLM Responses
Learn how to use output parsers in LangChain to format, validate, and transform raw LLM output into structured data. Covers usage, purpose, and code examples.
Articles about LLMs
· SuperML.dev · agenticAIHow to Build an Agentic Stock Analyst with LangChain and YFinance
Create an intelligent AI agent that performs stock analysis using real tools, technical indicators, economic data, and even triggers mock trades.
· SuperML.dev · ai-ml10 Real-World Applications of LLMs (Large Language Models)
From customer support to code generation, LLMs are transforming industries. Here's how they’re being used in the real world.
· SuperML Editorial TeamUnifying AI Agent Architectures: MCP, A2A, and LangChain
· SuperML.dev · ai-mlFrom Java to AI/ML: A Transition Guide for Engineers
Learn how experienced developers can transition from Java and enterprise software to the booming field of AI and Machine Learning with a roadmap, tools, and learning paths.
· SuperML.dev · agenticAILangChain Agents: Tool-Augmented Reasoning with LLMs
Learn what LangChain Agents are, how they work, and the problems they solve through dynamic tool invocation and decision making.