How to Achieve AI Consistency for IP Solutions in 3 Steps

Black Hills Ai
December 4, 2025

Ask an AI tool the same question twice, and you’ll likely get two different answers. This isn’t a bug; it’s a feature baked into how large language models (LLMs) work. The probabilistic nature of AI means that each response involves countless micro-decisions, with the model selecting from multiple plausible options at every step. For IP professionals accustomed to precision and consistency, this variability can feel unsettling, even unacceptable. Yet intellectual property firms are increasingly dependent on AI to draft patent applications, respond to office actions, and analyze prior art. The efficiency gains are too substantial to ignore. The trick becomes understanding how to achieve AI consistency for IP solutions that your professional, complicated work demands. The answer isn’t to fight AI’s inherent variability.

Companies like Black Hills AI, founded and run by former IP attorneys and professionals (not just software developers) with extensive experience who set out to create the generative AI tool they wished they had when they were in your shoes, have engineered a veritable game-changer, Otto IP™. This proprietary, generative AI solution is specifically engineered for IP nuances and built to address the high bar of precision and consistency demanded by legal professionals.

“One helpful way to think about it is like baking,” said Thomas Marlow, Chief Artificial Intelligence Officer at Black Hills AI. “You can’t control the exact behavior of yeast, but you can control the recipe, ingredients, temperature, and timing to get consistent results. This very same principle applies to AI tools.”

Achieve AI Consistency With IP Automation Solutions: 3 Steps

The principle of achieving consistency in Generative AI involves controlling the variables within the tool’s probabilistic nature. But that’s easier said than done. Here’s how to achieve AI consistency for IP solutions in 3 simple steps:

Choose the Right Model
Hone Your Prompting
Use Guided Skills to Encode Consistency into Your Workflow

1. Choose the Right Model

Not all AI models are created equal, and the gap between best-in-class and second-tier tools is widening. We’re living through an extraordinary period of rapid AI advancement.

Choosing the right product is key to understanding how to achieve AI consistency for IP solutions. Models that were state-of-the-art six months ago are now outperformed by newer versions that are faster, more accurate, and better at following complex instructions.

This creates both an opportunity and a challenge:

The opportunity: By using current, leading models, you get better baseline performance, a more nuanced understanding of context, and more reliable outputs.
The challenge: “Best-in-class” is a moving target that changes almost monthly – sometimes on a day-to-day basis.

For IP professionals, this means your AI tool selection may not be a one-time decision. You need to evaluate your needs and the tool you’re using continuously. You need vendors who actively update their underlying models as improvements become available.

A tool locked to an outdated model will produce increasingly inconsistent results compared to what’s possible with newer technology, not because it’s gotten worse, but because your expectations and benchmarks have evolved.

“A helpful tip to ensure you make the right choice now and in the future when evaluating AI tools is to ask not just what model they use today, but how frequently they update and whether those updates are automatic or require action on your part. With Otto IP™, this is never a concern,” said Marlow.

2. Hone Your Prompting

If you’ve experimented with ChatGPT or similar tools, small changes in how you phrase a question can yield dramatically different responses. This sensitivity to prompting is one of the primary sources of inconsistency in AI outputs.

The solution is disciplined consistency in three areas:

Prompt structure: Instead of asking questions casually or varying your approach, develop standardized prompt templates for recurring tasks. A patent claim analysis or response argument should follow the same prompt structure every time, with clearly defined sections for the prior art references, the claim language to analyze, and the specific questions you need answered.
Context provision: AI models can only work with what you give them. If you sometimes include the full specification and other times provide only the claims, you’ll get wildly varying quality. Establish standard practices for what documents and information to include for each task type.
Workflow sequence: Many IP tasks benefit from a multi-step AI interaction rather than a single prompt. For example, drafting office action responses might involve: (1) analyzing the rejection, (2) identifying the key issues, (3) developing arguments for each issue, and (4) drafting the response. Breaking complex tasks into consistent steps yields more reliable results than one-shot prompts.

“The challenge is that maintaining this discipline manually is tedious. Every attorney might develop their own approach, leading to firm-wide inconsistency. This is where Otto IP™’s built-in custom, guided workflows become incredibly valuable,” explained Marlow.

3. Use Guided Skills to Encode Consistency Into Your Workflow

A sophisticated approach to AI consistency is encoding best practices directly into the tool.

This is the idea behind guided skills: pre-built workflows that combine standardized prompts, specific LLM instructions, and contextual guidance into repeatable processes. Think of guided skills as the IP equivalent of surgical checklists.

A surgeon doesn’t rely on memory to ensure every safety step is followed in the same order every time. They use a checklist. Similarly, guided skills ensure that your AI interactions follow the same proven sequence, with the same context, every time.

Generative tools like Otto IP™ from Black Hills AI include over 100 guided skills for everyday IP tasks, patent office action responses, claim drafting, trademark likelihood-of-confusion analysis, and more.

Each guided skill embeds the prompt engineering expertise of experienced IP attorneys, providing a standardized starting point that any attorney at the firm can use. The beauty of this approach is that it democratizes consistency.

Junior associates benefit from optimized prompts developed by senior practitioners. And because guided skills can be customized and saved, firms can build their own internal best practices and share them across the team.

“As you refine your approach based on results, you update the guided skill once, and everyone benefits. This creates a virtuous cycle: better prompts lead to better results, which inform further prompt refinements, steadily improving consistency over time,” explained Marlow.

Call Black Hills AI

AI’s variability doesn’t have to mean an inconsistent work product if you learn how to achieve AI consistency with IP automation solutions. By controlling model selection, standardizing your prompts and workflows, maintaining review practices, and leveraging guided skills, you can achieve remarkable consistency, often exceeding what’s possible with purely manual processes where each attorney operates independently.

“The firms winning with AI aren’t the ones using it casually. They’re the ones treating it as a professional tool requiring professional discipline. They’ve recognized that consistency in AI outputs is engineered through deliberate choices and systematic processes. In IP practice, where precision and reliability define professional reputation, that engineering effort is the difference between AI as a liability and AI as an advantage,” concluded Marlow.