Learn to build the control logic for a multi-step AI research assistant. Structure the main python script to manage input, control iteration, and orchestrate modules for query generation, relevance checks, context extraction, and report creation. Implement an LLM-driven workflow for iterative search and comprehensive report generation.
Overview
Syllabus
- Unit 1: Setting Up the Basic Structure for DeepResearcher
- Building the DeepResearcher Framework
- Implementing the Main Research Workflow
- Adding Error Handling to DeepResearcher
- Unit 2: Generating Search Queries with OpenAI
- Designing Prompts for Search Generation
- Generating and Parsing Search Queries
- Validating AI Responses for Robust Research
- Testing Your Search Query Generator
- Unit 3: Parsing and Selecting Useful Information
- Collecting Web Content for Research
- Creating Prompts for Content Relevance Evaluation
- Evaluating Content Relevance with AI
- Designing Prompts for Information Extraction
- Extracting Gold from Web Content
- Unit 4: Iterative Search: Refining Research Through Multiple Rounds
- Building the Iterative Research Loop
- Accumulating Knowledge Across Research Rounds
- Creating the Research Planner Brain
- Safely Parsing LLM Query Responses
- Testing Your Iterative Research System
- Unit 5: Creating the Final Research Report
- Crafting Prompts for Research Reports
- Implementing the Final Report Generator
- Print the Final Report
- Testing Your AI Research Assistant
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