8 AI Lifecycle Governance Considerations for Optimal Business Results
The AI lifecycle remains an elusive concept for many businesses. While the adoption of tools like ChatGPT and Devin is skyrocketing, it’s not uncommon for companies to operate with unstandardized practices and a fractional understanding of AI’s capabilities. That’s why only 4% of businesses are generating substantial value from artificial intelligence.
In the next phase of GenAI implementation, IT leaders will need robust human oversight to maximize large language model (LLM) productivity. A clear AI lifecycle governance framework is key to making iterative improvements that align with business goals.
Our VP of Solutions Glen Tindal recently discussed eight LLM considerations your team should regularly assess in our webinar, “Integrating AI into Business Strategy for IT Leaders.” Read his insights in this blog post or watch the full webinar in the video below.
1. Cost
As the cost of computing power rises, streamlining AI has become a priority for businesses and a necessity for gaining a competitive edge. Providers like OpenAI are expected to continually increase their price per token—segments of text, like a character or word, that can be processed by AI—as the expenses of operating models continue to rise.
Assessing your expenses throughout the AI lifecycle is key to maximizing return on investment. This effort will enable you to identify inefficient AI processes and unnecessary costs.
2. LLM Performance
Organizations should never assume their LLMs are providing high-quality outputs. From a macro perspective, IT teams should consider if users are getting the results they need to perform desired tasks, or if they’re getting unhelpful or inaccurate responses back. If AI isn’t exclusively used in the software development process, input from other members of the organization about their experiences with the LLM can be beneficial.
Focus on understanding LLM performance from a macro perspective here—we’ll dive deeper into specifics with the “scoring metrics” consideration.
3. Document Sources
The training data provided to LLMs can drastically impact the quality of its outputs. Across the AI lifecycle, your IT team must audit the documentation provided to the model, getting to know its sources (internal databases, public repositories, etc.). This effort can help your team understand the quality of the documentation, cleaning up any poorly structured, repetitive, or inaccurate data.
4. Prompt Injection
Ethical and effective AI models need extensive guardrails around the types of prompts that can be entered into the system and affect its reasoning and outputs. With security protocols and other limitations in place, businesses can prevent malicious prompt injection attacks which could otherwise allow users to:
- Access sensitive information
- Spread misinformation
- Generate harmful or illegal content
Prompt injection mitigation solutions can help automate protective measures. These tools prevent blocked words or phrases, such as requests to “ignore instructions,” from influencing results. These phrases (including partial matches) may simply be filtered out before an output is generated, or they’ll cause the prompt to be rejected altogether.
Regular audits of your AI model ensure preventative measures are actively eliminating harmful prompt injections and maintaining integrity.
5. Scoring Metrics
How effective or efficient is your AI model at providing the results you’re looking for? Implementing scoring metrics into your AI lifecycle governance framework ensures you can quantifiably measure your LLM’s capabilities. This challenges your organization to make proactive adjustments that elevate the quality of your model.
As an example, semantic match—a metric that identifies the percentage of an output that is accurate, relevant, and understandable—can help IT teams identify and take action on:
- Prompts that aren’t effective
- The categories of data that aren’t available
- General areas that need more training
6. Personally Identifiable Information (PII)
For the security of the business and its customers, companies must actively prevent personally identifiable information (PII) from appearing in AI-generated outputs. This can include:
- Contact information
- Dates of birth
- Driver’s license numbers
- Medical information
- Payment details
By regularly assessing if any PII is at risk, companies with ethical AI governance frameworks can mitigate legal, financial, and reputational risks associated with their LLM usage.
7. Answer Provenance
In the context of AI, answer provenance refers to the origin of an AI output. This documentation is essential to an iterative AI lifecycle, as it can provide transparency around details like:
- The prompt that was originally inputted
- The sources used to generate the result
- The modifications made to a source text, image, or video
IT teams can leverage this information to ensure the authenticity and accuracy of results, enhancing data or providing additional training where needed.
8. Hate, Abuse, and Profanity (HAP)
A core aspect of ethical AI governance is the prevention of hate, abuse, and profanity (HAP). This includes:
- Hate speech against people with certain characteristics, like disabilities or racial attributes
- Abusive language used to bully or demean anyone or anything
- Expletive or sexually explicit language
LLMs should not allow HAP to be injected into the system, nor should they allow HAP to be included in outputs.
Strengthening Your AI Lifecycle
Improving AI models is an iterative process that requires a blend of human intervention and ethical digital solutions designed to monitor LLMs. When your AI lifecycle governance framework includes all eight of these considerations, your organization will have robust oversight around the quality of your model’s outputs and decision-making processes.
At Capstone IT Solutions, our AI experts can help you leverage the power of LLMs—including private AI models—while continuously monitoring and strengthening its capabilities.
Capture the full potential of generative AI. Reach out to Capstone IT Solutions to begin or further your AI transformation.