EGI — Guided Interactive Exploration
Atomic Claim
Overview
EGI is a multi-turn AI interaction pattern that systematically maps an unfamiliar knowledge domain through structured questioning sequences, preventing both premature closure and infinite exploration.
💡 The Pattern
EGI is used when entering an unfamiliar topic. It proceeds in three moves:
Move 1 — Panoramic mapping
"Give me a landscape map of [topic area]. What are the 5-7 major
sub-areas, the key open questions in each, and the 2-3 most
cited researchers or papers per area? Format as a structured outline."
Move 2 — Targeted drilling
"In the sub-area of [X from Move 1], what are the 3 most important
papers published since 2022? For each: main contribution,
key metric, and whether it is reproducible."
Move 3 — Gap identification
"Based on what you've described, what are the most significant
gaps in the literature on [X]? Which gaps are most tractable
for a 6-month MSc thesis with local compute?"
🧩 Application to PUMA
Used in F0 (Initiation) to map: LLM benchmarks landscape, PM+AI literature, prompting strategies for classification tasks. See: 60 - Resources/61 Prompts/61.1 LLM-Tools/PT-Claude-EGI-Exploration
🔗 Connected Ideas
Part of: PN-MIT-Student-Method | Uses: PN-RCOIF-Framework | Followed by: PN-AMI-Framework