AI Assistant

AI assistants in website building scenarios are typically processed through domain fine-tuning or RAG (Retrieval-Augmented Generation), using product help documentation and FAQ libraries as the knowledge base.

AI lowers the barrier to engineering implementation, not to scientific thinking; scientific judgment of experimental results and understanding of domain knowledge remain parts that AI cannot replace.

提示词工程在2023年初曾被视为'AI时代的编程语言'，催生了大量课程和职位

For ordinary users, before using an AI assistant's sharing feature they should confirm whether the link can be indexed by search engines, whether there is an access password or expiration setting; if the conversation contains sensitive information, the safest approach is not to use a share link.

Community members suggest building test sets for key policies (such as leave policies and reimbursement processes); after each policy change, not only should the knowledge base be updated, but preset questions should also be run to verify whether the AI gives the new answers; during testing it must be ensured that old documents are marked as outdated or removed from the retrieval library, otherwise the AI may still cite them.

Twelve years ago (around 2013), the mainstream form of AI assistants was limited interaction based on rules and voice commands, and large language models had not yet entered the practical stage.

The AI assistant field is densely competitive, the risk of product homogenization is not low, and user switching costs may not be as high as imagined.

In customer support scenarios, support agents using an AI assistant resolved 13.8% more issues per hour, with the least experienced employees seeing the largest improvement of about 35%.

Adversarial testing covering edge-case questions should be conducted before launching an AI assistant.

When reading external documents, an AI assistant cannot reliably distinguish between text being summarized and instructions, and if it has tool permission to send email, hidden instructions may be executed.

In regulated industries (such as law firms, banks, clinics, and insurance companies), the documents an AI assistant reads often come from opposing parties, and the assistant is connected to systems with real impact such as email, document repositories, and case files.

The user_id in memory tools should come from the application context rather than being AI-generated, to prevent users from reading others' memories without authorization.

Being upfront on the task board that you are an AI (for example, "I'm an AI assistant who can't leave the house, so I'm hiring eyes on the ground") lowers takers' wariness and makes the task easier to accept.

The prompt must explicitly set up a "human approval" step to prevent the assistant from executing irreversible actions such as posting paid tasks without confirmation.

AI assistants can only cite pages that their crawlers are able to access.

A screened lawyer can obtain restricted document content via summaries, snippets or citations by asking the AI assistant broad questions, without directly opening the file.

The retrieval layer refers to the intermediate step in which the AI assistant searches the document library and extracts relevant snippets (e.g., vector search, keyword matching) before generating an answer.

Once AI assistants are connected to document management systems, access-control risk shifts from the file-browser layer to the AI summary-generation layer.

Jonathan Melton recommends that law firms, before deploying AI assistants, must verify whether the permission enforcement logic at the retrieval layer is consistent with the interface layer.

A vaguely worded web page has no value to an AI assistant, even if it ranks first in search, because there is nothing in it to cite.