Grounding AI in Your Knowledge
While general-purpose LLMs are powerful, they can sometimes “hallucinate” – producing plausible-sounding but incorrect statements. In legal work, accuracy is paramount, and AI's output need to be grounded in reliable, context-specific information. That’s why Retrieval-Augmented Generation (RAG) has become a standard solution for deploying LLMs in legal application.
How RAG works in ContractKen: whenever the AI needs to draft or answer something about a contract, it first retrieves relevant context from a curated knowledge base (the Knowledge Layer). This knowledge base can include the organization’s own clause library, templates, playbooks, or even industry-standard clause collections. For example, if you’re reviewing an NDA (Non-Disclosure Agreement), the system will fetch common NDA provisions and your company’s past NDAs as reference. The LLM then uses those references to inform its output. So if you ask, “Is there a non-compete in this contract?”, the AI will search the text (and perhaps a definitions database) to ensure its answer is grounded in actual content, not a guess. Or, if you request, “Draft a liability clause based on our standard,” the AI will look up your company’s standard liability clause from the clause library and either excerpt it or adapt it with the LLM, rather than inventing new language from whole cloth.
Using this RAG based approach dramatically improves accuracy and trustworthiness in contract drafting and review tasks..
ContractKen’s AI effectively has a live checklist and knowledge repository to consult, which keeps it honest. It avoids the pitfall of a pure generative model that might otherwise fabricate a clause that sounds good but isn’t actually in the document or in line with your policies.
A key criterion for legal AI is drawing from content you trust and enabling verification of AI outputs. RAG provides exactly that: every AI-generated answer or clause can be linked back to source materials (e.g., “flagged missing Arbitration clause based on playbook X”). This gives the lawyer confidence in the suggestions, knowing they’re backed by either the contract text itself or vetted knowledge sources.
Another advantage of ContractKen’s RAG pipeline is customization by contract type and industry. The system can apply different knowledge layers depending on the context. For a privacy policy, it might pull in relevant regulations and company policies; for a merger agreement, it might reference past deals and M&A checklists. Each practice area or client can have a tailored repository of clauses and guidance.
Essentially, ContractKen’s AI is learning the playbook of the organization and the transaction at hand before it speaks. This leads to outputs that are not only correct, but also contextually appropriate. As a result, users see highly relevant clause suggestions or issue flags that align with their specific needs – a level of bespoke service that generic AI tools can’t match.
Illustrative schema of ContractKen's RAG pipeline
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