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Latest from Linyb Geek Road

94 recent articles

Linyb Geek Road

Apr 17, 2026 · Artificial Intelligence

Clarifying the Key Components of AI Large‑Model Development: Vectors, Vector Models, and RAG

This article explains how vectors encode text or images, how vector (embedding) models generate these numeric representations, why specialized vector databases are needed for similarity search, and how Retrieval‑Augmented Generation (RAG) combines them to produce reliable answers while stressing the necessity of using the same model throughout the pipeline.

AILarge Language ModelRAG

0 likes · 8 min read

Clarifying the Key Components of AI Large‑Model Development: Vectors, Vector Models, and RAG

Linyb Geek Road

Apr 16, 2026 · Artificial Intelligence

Does Conway's Law Apply to LLM Agent Systems? Design Insights and Best Practices

The article explores how Conway's Law—"organizations design systems that mirror their structure"—extends to large‑model agent architectures, offering concrete examples, role‑alignment strategies, concise communication patterns, and cautions against over‑engineering to improve multi‑agent collaboration.

AI CoordinationAgent System DesignConway's Law

0 likes · 9 min read

Does Conway's Law Apply to LLM Agent Systems? Design Insights and Best Practices

Linyb Geek Road

Apr 15, 2026 · Artificial Intelligence

Designing a Stateful Multi‑Turn Dialogue Agent on Stateless LLMs

Building a production‑grade multi‑turn dialogue agent requires managing LLM’s statelessness by combining sliding‑window and summary history, implementing three‑layer memory (working, short‑term, long‑term), using explicit state tracking with incremental JSON updates, optimizing context windows, orchestrating tool calls, and adding meta‑control to handle failures and prompt‑injection risks.

LLMcontext windowmemory system

0 likes · 18 min read

Designing a Stateful Multi‑Turn Dialogue Agent on Stateless LLMs

Linyb Geek Road

Apr 15, 2026 · Artificial Intelligence

How to Optimize Prompts for Multi‑Turn Large‑Model Dialogues

The article outlines practical methods for designing and refining prompts in multi‑turn conversations with large language models, covering task definition, contextual information, structured templates, step‑by‑step guidance, knowledge‑graph integration, dynamic adjustments, and real‑time data incorporation, each illustrated with concrete examples and code snippets.

AILarge Language ModelPrompt Engineering

0 likes · 11 min read

How to Optimize Prompts for Multi‑Turn Large‑Model Dialogues