Not long ago, the idea of automating high-frequency legal queries was a futuristic concept, something out of a sci-fi movie. Today, it’s a reality, facilitated by advanced AI technologies. However, with these advancements, in-house legal teams are faced with a conundrum: build, buy, or co-build AI assistants?
The Complexities of Building Your Own AI Assistants
While the advent of powerful Large Language Models (LLMs) and their APIs makes it easier than ever to create a rudimentary AI product, this 'thin layer' approach often lacks depth and fails to comprehend and address the intricate nature of legal operations.
Building an AI solution from scratch, without leveraging appropriate libraries or toolkits, is not dissimilar to coding a website's frontend without relevant tools; it's possible, but not necessarily efficient or comprehensive.
Buying or Co-Building: Mitigating Complications
Rather than attempting to build an AI assistant from scratch, a more effective approach may be to buy or co-build with a seasoned vendor who already possesses the appropriate AI toolkit and domain expertise.
One emerging trend is the adoption of domain-specific toolkits, which enable companies to configure, customize, and scale AI tools swiftly. This combines the best aspects of buying —deep domain expertise and established AI toolkit— and building —customization to suit specific needs.
Experienced vendors like Legal OS are pioneering this approach, supplying a toolkit built on the insights derived from hundreds of thousands of API calls, allowing you to design AI assistants that seamlessly fit your workflows.
Whether buying or co-building, it's pivotal to ensure the AI solution slots into existing workflows without introducing new friction points. Successful AI solutions like a chatbot should answer end-user's questions seamlessly and almost indistinguishably from a human, not introducing friction or disrupting workflows.
Leveraging a Richer Architecture and Consideration of Context
We must remember that AI is not a solution in itself, but a facilitator; a feature within a more complex architecture, requiring numerous programmatic steps, models and modules.
One prime example is the management of legal queries using AI. A question may lack sufficient detail for an LLM to execute a meaningful search outright. A strategy here is to employ additional modules to enrich the query with context, making it digestible for the AI.
For instance, a question as vague as "Can we remove the logo clause?" may be translated to "An enterprise prospect would like to remove clause 3.1 of our terms, preventing us from using their logo without permissions."
Legal OS's architecture incorporates over 20 modules tailored for such tasks within legal processes. These modular solutions help to break down complex tasks into manageable steps, ensuring that queries are comprehensively addressed.
An Iterative Approach: Prompt Pipelines
Tackling complex tasks such as handling prospect objections or reviewing third-party documents often require an iterative approach rather than a single prompt. Take, for instance, a query "customer doesn't like clause 3.2." A single prompt approach could leave critical questions unanswered: What is clause 3.2, and which contract is being redlined?
In such scenarios, it makes more sense to build an initial prompt that enriches the query with relevant context and outputs a complete query, followed by subsequent prompts to address the complexity of the task.
The Future of AI in Legal Teams
In the intersection of build or buy, stepping into a co-building strategy with seasoned vendors such as Legal OS can enhance the efficiency and effectiveness of deploying AI assistants to resolve high-frequency legal queries. This approach reduces resource strain, introduces less friction and ensures the relevancy of the tool to your needs.
