Blogs of LLM Application

Contact me

• Blog -> https://cugtyt.github.io/blog/index
• Email -> cugtyt@qq.com
• GitHub -> Cugtyt@GitHub

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本系列博客主要是关于大语言模型的应用。

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Massive Search: LLM Structured Outputs is All You Need

Executing complex searches across entities with diverse attributes —such as text, numbers, booleans, and images—can be challenging. These searches often require intricate queries, potentially involving joins across multiple data sources. For example, searching for a book might involve filtering by its title, description, price, user comments, and cover image simultaneously.

Massive Search provides a method for querying such complex but logically grouped data by leveraging LLM Structured Outputs. “Logically grouped” means all the data pertains to the same core entity, like a specific book product.

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Smart Diagnosis Solution

Smart Diagnosis Solution, stack and layers

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Manufacturer-Executor-Evaluator: A General LLM Agentic Pattern for Collective Intelligence

• Manufacturer is responsible for generating the task specification based on the task examples, which is the system start point and objective,
• the Executor is responsible for executing the task based on the task specification, it is the final solution output,
• and the Evaluator is responsible for evaluating the execution result to make sure the task specification meets the objective, feedback or comments from Evaluator will be used to improve the task specification in the next iteration.

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Prompt Factory

Prompt Factory help user to write prompt from provided samples

Writer generates prompt

Actor practices prompt

Critic evaluates prompt

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软件实现Copilot的架构图

软件实现Copilot的架构图

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一个应用的Copilot要怎么做

简单介绍如何给一个应用做Copilot

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LLM 做文档问答应用

LLM具有很强的文字总结能力，结合文档检索可以做文档的智能问答，本文将介绍在微软内部的一些实践。

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LLM Patterns - 中文

Extractor 单元

Composer 单元

Converter 单元

Router 单元

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LLM Patterns

Extractor unit

Composer unit

Converter unit

Router unit

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