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📂 AI Agents & Applications

7 posts

Part 2.1: Summarizing Text with LangChain

Learn practical techniques for summarizing large documents and multiple documents using LangChain, LCEL, and advanced strategies like MapReduce.

#llm#langchain#summarization

Part 2.2: Summarizing Across Documents

Learn how to summarize information from multiple data sources using MapReduce and Refine techniques with LangChain document loaders.

#llm#langchain#summarization

Part 2: Summarization

Learn practical techniques for summarizing documents, building research engines, and creating agentic systems with LangChain and LangGraph.

#llm#summarization#langchain

Part 1.4: Executing Prompts Programmatically

Master prompt engineering techniques for text classification, sentiment analysis, summarization, composing text, question answering, and reasoning with practical examples.

#llm#prompt-engineering#chatgpt

Part 1.1: Understanding AI Applications

Learn about LLM-based applications, chatbots, and AI agents—understand their differences and when to use each pattern.

#llm#ai-agents#chatbots

Part 1.2: LangChain Framework

Explore LangChain architecture, core object model, and how it simplifies building production-grade LLM applications.

#llm#langchain#architecture

Part 1.3: Making LLMs Smarter

Master prompt engineering, RAG, fine-tuning, and learn how to choose the right LLM for your application needs.

#llm#prompt-engineering#rag