Build an AI Research Tool with LangChain, Gemini, and GPT-4o

Modern academic and market research requires synthesizing vast amounts of complex literature quickly and accurately. This text-based course guides you through developing an automated research assistant using LangChain, Python, and leading foundation models. You will gain the practical skills needed to query academic sources like Google Scholar, manage multi-step reasoning with LangChain agents, and evaluate the performance of Gemini Pro versus GPT-4o for document summarization and citation tracking. What you will learn: - Understand the core architecture of LangChain, prompt templates, and autonomous agents - Configure search integrations to query, parse, and structure academic literature - Implement retrieval-augmented generation patterns to summarize complex papers accurately - Compare and evaluate output quality, token economics, and reasoning styles between Gemini Pro and GPT-4o - Apply structured output parsing to generate clean research reports with verified citations - Practice building error-handling routines and rate-limit management for robust API usage You will start by learning key terminology, fundamental LLM concepts, and API setup before moving into step-by-step code walkthroughs for data retrieval, prompt construction, and model benchmarking. This course is designed for programmers, data analysts, and researchers familiar with basic Python syntax who want to apply modern generative AI to automate scholarly investigations without needing prior machine learning expertise. Start reading today to build your own intelligent literature review assistant.