Create an intelligent AI agent that performs stock analysis using real tools, technical indicators, economic data, and even triggers mock trades.
Learn what LangChain Agents are, how they work, and the problems they solve through dynamic tool invocation and decision making.
Learn how to use Chains in LangChain to create structured, multi-step workflows with prompts, tools, and memory. Includes hands-on examples.
Learn how to use document loaders, text splitters, and vector stores in LangChain to enable retrieval-augmented generation (RAG) and semantic search.
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.
Master PromptTemplate in LangChain with examples and best practices. Learn how to structure dynamic prompts for LLMs in real-world applications.
Learn how LangChain helps you build powerful, real-world AI applications using LLMs. From chatbots to agents, LangChain simplifies development.
From customer support to code generation, LLMs are transforming industries. Here's how they’re being used in the real world.
This post explores a novel two-stage AI system combining deep learning and large language models to predict and explain shock events in ICU patients, enhancing transparency and trustworthiness in critical care AI.
