Letting the AI run wild

While AI’s creative capabilities are impressive, they come with inherent risks. With proper guardrails, the outputs can become predictable and manageable.

AI can produce off-topic, irrelevant, or even inappropriate content without proper constraints. As a result, businesses and content creators might get hurt. For instance, an AI writing tool might generate marketing copy that is in the wrong tone or even offensive, which can damage a brand’s reputation.

Controlled creativity can generate content that aligns differently with the brand’s voice or message. The end goal, of course, is clarity and consistency.

Guardrails are critical for generative AI

Given these risks, it’s clear that guardrails help control AI’s creative Letting the AI run wild potential. Here’s why guardrails are crucial:

Maintaining relevance and focus:

Guardrails help keep the AI’s outputs focused on the intended topic, preventing deviations that can dilute the message.

Ensuring appropriateness:

Guardrails protect your brand’s reputation and ensure that the content suits your audience by filtering out inappropriate or offensive content.

Aligning with brand voice:

Guardrails ensure that AI-generated content is consistent with your brand’s voice and tone, maintaining coherence in your messaging.

Enhancing credibility:

By preventing factual inaccuracies, guardrails enhance the credibility and reliability of AI-generated content, especially in fields that require precision.

Optimizing user experience:

Well-implemented guardrails contribute to a better user experience by ensuring the content is engaging, relevant, and valuable to the audience.

The following sections will explore practical techniques for providing these guardrails to manage AI creativity effectively.

Techniques for providing guardrails

Effective guardrails for AI are strategies that can help control the output, ensuring it meets specific requirements and aligns with your objectives.

Keyword filtering

Without limiting what the LLM does, it loves to come up with sentences/words like: “In the ever-evolving landscape of…” and “As we stand on the cusp of this new era, the possibilities are as limitless as our imagination.” It uses long-winded sentences with very expressive language, full of cliches. You can curb this by limiting the words or expressions it can use.

Keyword filtering involves setting up filters to exclude specific words, phrases, or types of content deemed inappropriate, irrelevant, or not aligned with your brand’s voice. This technique is useful for maintaining content suitability and relevance.

It’s not hard to implement:

• Identify keywords: List words or phrases that should be excluded. This can include offensive language, jargon, or off-topic terms.
• Set up filters: Use AI tools that support keyword filtering. Configure these tools to flag or exclude content containing the identified keywords.
• Continuous monitoring: Regularly update the list of keywords based on feedback and new requirements.

Try this as an experiment. You’ll notice it’s fairly easy to influence what chatbots use and don’t use.

Write a short piece on the future of content creation with generative AI. Don't use the following words:

Buckle up
Delve
Dive
Elevate
Embark
Embrace
Explore
Discover
Demystified

but do use:

Unleash
Unlocked
Unveiled
Beacon
Bombastic
Competitive digital world

You can also make this process more proficient and scalable using APIs to communicate with LLMs and chatbots.

Prompt engineering

Prompt engineering involves writing prompts to guide the AI in generating content that meets the criteria. Leo S. Lo from the University of New Mexico developed the CLEAR method (context, limitations, examples, audience, requirements), an effective approach to prompt engineering. Of course, there are plenty of other ways to write great prompts for your content.

A practical example of using the CLEAR framework

Imagine we are creating content for a travel blog. Using the CLEAR framework, we devised the following prompt to inspire the AI chatbot to create a blog post about Kyoto, Japan.

Prompt: “Describe a day in the life of a local in Kyoto, Japan. Focus on their morning routine, interactions with neighbors, and favorite spots in the city. Use a descriptive and engaging tone to captivate travel enthusiasts. Include at least two historical landmarks and one local cuisine.”

1. Clear: The instructions are straightforward to understand. We specifically ask for a description of a day in the life of a local in Kyoto, including particular elements like their morning routine, interactions, and favorite spots.
2. Logical: The prompt is logically structured. It begins with a general description of a day in the life and then narrows down to specific details such as the morning routine, interactions with neighbors, and favorite spots. This logical flow helps generate a coherent and comprehensive piece of content.
3. Engaging: The tone is described as “descriptive and engaging,” which is crucial for captivating travel enthusiasts. The prompt invites the writer to create a vivid and relatable narrative by focusing on personal interactions and favorite spots.
4. Accurate: The prompt asks for at least two historical landmarks and one local cuisine. This ensures that the description is rooted in Kyoto’s actual cultural and historical elements.
5. Relevant: The topic is highly relevant to travel enthusiasts interested in different places’ cultural and daily life aspects. The prompt taps into a subject of high interest by focusing on Kyoto, a city known for its rich history and cultural landmarks.

Enhanced prompt

To refine it even further, you can add a few more specific guidelines to enhance clarity and completeness:

“Describe a day in the life of a local in Kyoto, Japan. Focus on their morning routine, interactions bolivia telegram data with neighbors, and favorite spots in the city. Use a descriptive and engaging tone to captivate travel enthusiasts. Include at least two historical landmarks (e.g., Kinkaku-ji, Fushimi Inari Taisha) and one local cuisine (e.g., yudofu, kaiseki). Ensure the narrative captures the essence of Kyoto’s culture and daily life.”

Why these improvements work:

• Clear: Specific examples such as Kinkaku-ji and yudofu provide clarity.
• Logical: The flow from morning routine to interactions and favorite spots remains logical.
• Engaging: The descriptive and engaging tone is maintained.
• Accurate: Named landmarks and cuisines ensure accuracy.
• Relevant: Provides a detailed, culturally rich experience relevant to travel enthusiasts.

Now, the prompt is well-crafted and aligns with the CLEAR framework, and the enhanced version provides additional guidance and specificity.

Template usage

Templates provide a structured framework the AI chatbot can follow, ensuring consistency and completeness in the generated content. Templates can be particularly useful for recurring content types like blog posts, reports, product descriptions, etc. Using templates, you can maintain a uniform structure across different pieces of content. As a result, all necessary elements are included and appropriately organized.

• Identify common content types: Determine the types of content you frequently generate, such Analyzing how did you come up with the idea for the entrepreneur hero award and what exactly is it about? and as blog posts, product descriptions, social media posts, etc.
• Create templates: Develop templates for each content type. These templates should include sections and prompts for each part of the content.
• Provide clear instructions: Include detailed instructions within each template section to guide the AI. This can involve specifying the tone, style, length, and key points to cover.
• Consistent use: Use these templates consistently to maintain uniformity across all generated content. Review and update the templates regularly to reflect new requirements or insights.

Parameter tuning

Adjusting parameters like temperature and top_p can control the randomness and loan data creativity of the AI’s output. This might seem like it controls creativity, but that’s not actually the case. Instead, it fine-tunes how the model balances creativity with coherence. Temperature affects the variability of the generated content, while top_p controls the diversity by sampling from a subset of probable tokens.