Usage in detail
Last updated
Last updated
The ClauseBase platform makes use of LLMs in very diverse situations. Because the use of LLMs involves quite some concerns from legal teams and compliance officers, we describe each individual usage of LLMs within ClauseBuddy, including the customer data that gets sent to the LLM.
Note that for each of the situations described below, ClauseBase merely passes along the relevant data to the LLM. In other words, the ClauseBase platform does not store the customer data that it passes to the LLM, except if the user would afterwards deliberately store the results in ClauseBuddy's database.
For example, if the user selects two pages of highly confidential text from a share purchase agreement in an MS Word document, and asks ClauseBuddy to summarise that text, then ClauseBuddy will merely pass along that text to the LLM, and present the summary to the user. It is then up to the user to decide what to do with the text — e.g., the user may decide to copy that summary into some new Word document, or perhaps even to store the summary as a new "clause" in a ClauseBuddy library. However, except for what the user decides to do, ClauseBuddy does not remember either the initially selected text or the summary.
Do note however that with each request, the ClauseBase platform will also send an internal reference (composed of the ClauseBase's internal customer ID and internal user ID) to Microsoft's servers, as part of the technical "user" field in the API call to Microsoft' server. The purpose of this internal reference is for fraud prevention: if ever malicious usage would be detected, the combination of Microsoft's logging and ClauseBase's customer ID + internal user ID should allow to track the malicious user. Also note that Microsoft may keep a copy of the instruction for a limited period of time, for fraud prevent purposes. ClauseBase has no influence over Microsoft's position in this regard.
Users can instruct Clause9 to automatically draft the filename of a clause on the basis of the clause's contents. No data other than the current clause's contents gets sent to the LLM, in the currently selected language.
ClauseBuddy allows users to draft a prompt (typically for creating a custom clause), which is then sent to the LLM. The answer can be directly copied or inserted into an MS Word document or Outlook email.
No other customer data than the prompt gets sent to the LLM.
When using ClauseBuddy within MS Word, users can request the LLM to redraft the currently selected text. The currently selected text in MS Word then gets sent to the LLM.
When using ClauseBuddy within MS Word, users can request the LLM to draft a summary of the currently selected text. No other customer data than the currently selected text gets sent to the LLM.
ClauseBuddy also allows users to draft an entire document on the basis of a prompt.
Initially the LLM is provided with the first prompt of the user (e.g., "Draft me a short consultancy agreement between client X and counterparty Y").
The LLM will subsequently draft a table of contents and this to ClauseBuddy.
The user can then choose to fill individual clauses with either content from his own clause library (for which the LLM doe not get involved), or content drafted by the LLM following a new instruction. In the latter case, the LLM gets sent the new prompt.
Users can also ask the LLM to provide suggestions for redrafting existing clauses within the table of contents, or for adding subclauses. In such case, the LLM gets sent the content of the current clause.
When using ClauseBuddy within MS Word, users can request the LLM to "explain" the currently selected text — e.g. to provide a more readable version of a very technically inclined text. Similarly to "Draft summary", no other customer data than the currently selected text gets sent to the LLM.
Users can instruct ClauseBuddy to automatically draft the filename of a clause, on the basis of a summary of the clause's contents, by clicking on the "Summary" button.
Alternatively, users can click on the "Keywords" button to draft a filename as a set of five keywords.
In both cases, no customer data other than the current clause's contents gets sent to the LLM, in the currently selected language.
Users can instruct ClauseBuddy to automatically anonymise the body of a clause, to remove typical confidential data (e.g., customer names, addresses, etc.).
Only the body of the currently selected clause will be sent to the LLM; no other customer data gets sent.
Please note the irony of this anonymisation feature. Anonymisation is actually a very hard problem, for which a significant level of intelligence is required from AI. Accordingly, only the latest AI-models (such as GPT4) are reasonably capable of this task. At the same time, many legal experts fear exactly those AI-models for confidentiality reasons.
ClauseBuddy can automatically guess relevant "attributes" (metadata) for each clause.
When the "Automatic" button gets clicked, the currently selected clause body, as well as the list of all potentially relevant but yet unused attributes, gets sent to the LLM. The LLM will then respond with a subset of relevant attributes.
Users can ask the LLM to redraft clauses stored within ClauseBuddy, by submitting a prompt.
The current contents of that clause will then get sent to the LLM, along with the prompt.
Optionally, users may also ask the LLM to automatically or semi-automatically adapt the terminology of the clause, so that it gets aligned with the terminology of the currently opened document in MS Word. In such case:
the currently opened document in MS Word will be sent to the ClauseBase platform, in order to extract the relevant terminology.
the LLM will only receive a list of the terminology that was compiled by the ClauseBase platform. In other words: the LLM does not receive a copy of the currently opened document.
ClauseBuddy's full document review feature allows users to request the LLM to review their currently opened document, on the basis of the user's own reviewing rules.
When performing such review, the document's contents will obviously be sent to the LLM, together with the rule set selected by the user.
For the avoidance of doubt: in the following scenarios, no LLM is involved. Instead, only the ClauseBase server is involved:
When the currently opened document is being proofread, or its definitions are being analysed, the entire document gets sent to the ClauseBase server. As will be evident on the basis of the speed of the analysis (usually less than a few seconds for even a 50 page document), this does not currently involve the use of any LLM.
Users can search within their currently opened Word-document for text that is semantically related to a search term.
The contents of the entire document gets sent to the ClauseBase platform for semantic analysis. The ClauseBase platform has its own local semantic vector database, so does not involve any third party LLM in this analysis.
ClauseBuddy can automatically extract clauses from uploaded documents (DOCX, PDF or scans). Those documents get sent to the ClauseBase platform for clause extraction purposes, but — as will also be evident from the high speed of analysis — no LLM is involved.
ClauseBuddy's AutoSuggest feature will present clauses that are semantically related to the currently selected clause in the currently opened MS Word document.
ClauseBuddy will sent the currently selected paragraph to the ClauseBase platform for analysis and semantic search, but no LLM gets involved. (Also here, speed is one of the determining factors: the search results are usually presented in less than 0.3 seconds).
ClauseBuddy's Smart Templates feature only makes use of LLMs for automatically generating questions & so-called "cards" on the basis of the cyan-highlighted text fragments inside of the DOCX file that got uploaded to ClauseBase's server.
In this situation, paragraphs that contain cyan highlights may get sent to the LLM. (Behind the scenes, ClauseBuddy chooses a set of paragraphs: if several paragraphs contain a certain cyan-highlighted identifier, then maximum two of them will ultimately get sent to the LLM, in order to not overload the LLM.)
The advanced full-document automation features of Clause9 currently only use LLMs for automatically creating the title of a clause on the basis of that clause's contents.
For completing a template, the use of an LLM does not make much sense, as this would be too slow and too unpredictable.
