推特中文区女菩萨参观号

See how free u r

[推特中文区女菩萨参观号 @larry_d0201]

@wangzan101 还是 vscode 用着方便，claude 和 gpt 和谐共存

[Ando @ando_w]

今天面试了个候选人，简历写"精通 RAG"。

我问召回率多少，他说"挺好的"。我问怎么评估的，他说"问了几句感觉不错"。

这就是行业现状：90% 的 RAG 项目没有评估集，全靠手感。能说出 Recall 和 Precision 的，已经是前 10%。

[推特中文区女菩萨参观号 @larry_d0201]

@uuuozi 小心机太多了

[文娅 @higgskids]

边爱爱边看片才会更有感觉
@DLjingxiang
@DLdaxiong
@DLxiaofufu
@DLtougao

[老金 @freeman1266]

根据官方指南写个 loop，周末跑一下看看效果如何

[Akshay 🚀 @akshay_pachaar]

RAG vs. Graph RAG vs. Agentic RAG, clearly explained!

Standard RAG embeds documents into vectors and retrieves the most similar chunks via similarity search. For direct factual lookups, this works well.

But it breaks down when a query needs to connect facts spread across multiple documents. Similarity search retrieves individual chunks, not the relationships between them.

Graph RAG adds a knowledge graph layer on top.

→ During indexing, an LLM extracts entities and relationships from the documents.

→ During retrieval, the system traverses these connections instead of relying on embedding similarity alone.

This is what enables multi-hop queries.

Say a vector DB stores three facts about internal services:

↳ "The checkout service uses payments API."
↳ "The payments API runs on cluster-3."
↳ "Cluster-3 is scheduled for maintenance on Friday."

Someone asks: "Will the checkout service be affected by Friday's maintenance?"

Vector search can likely retrieve facts 1 and 3 because the query mentions "checkout service" and "Friday maintenance."

But it will miss fact 2, which connects the payments API to cluster-3.

That middle fact sits too far from the query in embedding space. It mentions neither "checkout" nor "maintenance," so it never makes it into the retrieved context.

A knowledge graph connects these as linked entities, and graph traversal finds the full path in one query.

Agentic RAG takes a different approach entirely.

Instead of a fixed retrieval pipeline, an LLM agent decides at query time which tools to invoke, which sources to query, and in what order.

Check the visual below to understand the three architectures thoroughly.

One thing to note here is that these three aren't levels of sophistication that you need to graduate through.

Instead, they solve different query types.

↳ Single-hop factual lookups → standard RAG
↳ Multi-hop relationship queries → Graph RAG
↳ Dynamic multi-source tasks with tool use → Agentic RAG

----

Each of these architectures gets better when the underlying retrieval layer is efficient.

I recently wrote about a new RAG approach that cuts corpus size by 40x, reduces tokens per query by 3x, and improves vector search relevance by 2.3x.

The article is quoted below.

[卡颂 @kasong2048]

为什么之前 Superpowers 很火，最近 Grill with Docs ？���为他们是 Agent 发展不同时期的最佳实践

Superpowers 的流程会输出详细执行计划，这意味着他假设：
1. Agent 无法执行长程任务，所以需要 Plan 的 TODO 作为 Compact 后的执行锚点
2. Agent 长程执行会有偏差，所以执行计划需要细致到“每一步做啥”

Grill with Docs 的 /implement 就 5 句话，定义了一个标��的 Goal 格式：
- 目标是什么：去查 PRD 或 issue
- 怎么做：TDD
- 执行约束：定期执行测试和类型检查
- 通过标准：使用 /review 验收通过
- 交付标准：提交 commit

本质是：假设 Agent 已经有好用的 Goal 能力

[推特中文区女菩萨参观号 @larry_d0201]

@JingYi_sama00 @QingqingDomS 就不能说我小吗

[推特中文区女菩萨参观号 @larry_d0201]

@zjp1997720 了解了，那确实得装，sub agent 只是给个启动词最后拿结果

[推特中文区女菩萨参观号 @larry_d0201]

@SpursLegion 希望明年尼克斯还能打进总决赛，让马刺完成复仇

[推特中文区女菩萨参观号 @larry_d0201]

@hicasamadim 72

[推特中文区女菩萨参观号 @larry_d0201]

https://t.co/1WZKdI6ynL

[推特中文区女菩萨参观号 @larry_d0201]

@NBA