Imagine you are getting ready to drive home from work. Subconsciously, your brain accesses the memories and mechanics associated with the physical act of driving. It stands to reason that your brain would next uncover the memory and processes associated with navigation. In any case, you would not attempt to recall everything you have ever seen or experienced and simply hope the right information appears at the right time.
How does this relate to AI? Modern artificial intelligence works best in much the same way. Instead of attempting to reason over everything it has learned at once, effective AI systems retrieve and apply only the most relevant context needed to complete a specific task.
This becomes especially important for complex, high‑risk tasks such as analyzing contracts. When an AI model follows a structured, step‑by‑step reasoning process - rather than jumping straight to a conclusion - it produces results that are more accurate, understandable, and trustworthy for human decision‑makers.
Large language models (LLMs) are designed to predict and generate language based on patterns learned from massive training data. While powerful, many AI models operate as black boxes, producing answers without showing how the model arrived at the result.
For organizations managing contracts, this lack of visibility creates risk. When an AI model analyzes contract sentiment, auto-redlines, or identifies risk, stakeholders might want to understand why the model performed that action - not just the final answer.
Tasks that require legal interpretation, risk analysis, or compliance checks are not simple text‑generation problems. These are complex tasks that require intermediate reasoning steps, such as:
Without step‑by‑step reasoning, an AI model may produce results that look confident - but are impossible to validate.
Chain of thought prompting is an approach that encourages the model to reason through a problem in stages instead of jumping straight to the outcome. Rather than returning only a final answer, the model performs intermediate reasoning steps internally to improve accuracy and reliability.
For enterprise environments, the value of chain‑of‑thought is not exposing raw reasoning, but helping ensure the model performs logical, explainable analysis that can be reviewed, audited, and trusted.
In contract management, tasks requiring transparency include:
If an LLM performs its actions without explainability, it can introduce legal and compliance risk - especially in highly regulated industries.
CobbleStone Software's VISDOM® AI was designed specifically for contract lifecycle management (CLM), and not general-purpose contract generation.
VISDOM uses large language models in combination with Retrieval Augmented Generation (RAG) to help ensure the model reasons only over authorized contract data. The reasoning process follows a controlled, auditable sequence:
This approach helps ensure the model performs intermediate reasoning steps while keeping outputs explainable and compliant. CobbleStone does not use client data for AI model training. Instead:
For all these reasons, VISDOM performs reasoning without compromising privacy or compliance.
Chain-of-thought AI isn't about showing internal reasoning; it's about promoting reasoned, explainable decisions. CobbleStone's VISDOM AI applies these principles to contracts, where accuracy, auditability, and trust matter most.
When contracts carry financial, legal, and regulatory pressure, organizations need AI that can show its work without expositing sensitive logic or data.
CobbleStone contract management software delivers exactly that.
Book a free demo today! It's free - and risk-free.
*Legal Disclaimer: This article is not legal advice. The content of this article is for general informational and educational purposes only. The information on this website may not present the most up-to-date legal information. Readers should contact their attorney for legal advice regarding any particular legal matter.