A-Guide-to-Retrieval-Augmented-LLM
an intro to retrieval augmented large language model
虽然当前 LLM 的训练数据量已经非常庞大，动辄几百 GB 级别的数据量，万亿级别的标记数量 ( Token )，比如 GPT-3 的预训练数据使用了3000 亿量级的标记，LLaMA 使用了 1.4 万亿量级的标记。训练数据的来源也十分丰富，比如维基百科、书籍、论坛、代码等，LLM 的模型参数量也十分巨大，从几十亿、百亿到千亿量级，但让 LLM 在有限的参数中记住所有知识或者信息是不现实的，训练数据的涵盖范围也是有限的，总会有一些长尾知识在训练数据中不能覆盖到。
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/wang-shuo-a-guide-to-retrieval-augmented-llm — read its card at https://meshkore.com/agent/wang-shuo-a-guide-to-retrieval-augmented-llm/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/wang-shuo-a-guide-to-retrieval-augmented-llmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/wang-shuo-a-guide-to-retrieval-augmented-llm/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own A-Guide-to-Retrieval-Augmented-LLM?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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