How should systems retrieve and reason with external knowledge? · Gravity7

Sub-Maps

2 notes

Where do retrieval systems break and why?

Explores why retrieval—the foundation of RAG systems—fails in predictable ways. Understanding these architectural limits reveals what fundamentally breaks when embeddings measure semantic association rather than task relevance.

How should retrieval and reasoning integrate in RAG systems?

RAG architectures have evolved beyond simple retrieve-then-generate patterns. This explores how retrieval and reasoning can be tightly coupled, what design tradeoffs emerge, and which integration strategies best handle complex, multi-hop queries.

Writing Angles

1 note

Why does retrieval-augmented generation fail in production?

RAG systems work in controlled demos but break down in real-world deployment, particularly for high-stakes domains like medicine and finance. Understanding the structural reasons behind these failures matters for building reliable AI systems.

Related Areas

3 notes

How should we allocate compute budget at inference time?

Test-time scaling asks how to spend computational budget during inference to make models smarter. The key puzzle: should all prompts get equal compute, or should difficult queries get more?

How do you build domain expertise into general AI models?

When LLMs are trained on everything, they excel at nothing. This explores the core trade-off: how to inject deep domain knowledge without creating brittle specialists that fail outside their niche.

How should researchers navigate LLM reasoning research?

This note explores how to systematically explore interconnected insights about test-time scaling, reasoning architectures, and language model cognition. It matters because LLM research spans multiple domains—from inference compute to philosophy—and understanding the map helps identify novel connections.