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What Do Top Experts Know That Your AI Doesn't? (And How to Replicate It)

When you're developing AI products for specific domains, it's like putting together a complex puzzle. You've got all these domain-specific elements - specialized knowledge, industry practices, unique challenges - but you might not be an expert in all these areas.

Ideally, you'd be working closely with a principal domain expert who knows this specific field inside and out. They could guide you through the domain-specific complexities and help you make informed decisions that align with the industry's needs.

But what if you don't have access to such a domain expert? Maybe your budget is tight, or perhaps the right expert for that specific field isn't available. Don't worry! There's still a smart way to tackle this challenge of building domain-specific AI products without breaking the bank or compromising on quality.

The Challenge

Many AI developers find themselves stuck when working on domain-specific products, unsure of how to move forward. This lack of expertise in the particular domain can slow down progress and lead to less effective solutions. It's frustrating when you know your AI product has potential in a specific field, but you're not quite sure how to tailor it to that domain's unique needs.

The main issues when developing domain-specific AI products are:

  1. Limited knowledge in the specialized field where the AI will be applied
  2. Difficulty in making informed decisions about product direction that align with domain-specific requirements
  3. Slower progress due to time spent researching unfamiliar industry-specific topics
  4. Risk of missed opportunities or suboptimal solutions that don't fully address the domain's challenges
  5. Potential misalignment between the AI's capabilities and the actual needs of the target industry

The Solution

While working directly with a principal domain expert is the ideal scenario for developing domain-specific AI products, it's not always possible. When you can't have a domain expert by your side, here's a smart alternative: learn from people who already know the specific domain inside and out, but do it indirectly. Think of it as getting advice from the smartest person in that particular field, without having to pay for their time or even meet them in person.

Here's how you can do this:

  1. Find the Right Domain Experts: Seek out well-known professionals in the specific field related to your AI product. For example, if you're developing an AI tool for copywriting, find respected advertising copywriters or successful content marketers. Observe how they create engaging headlines, organize their content, and adjust their writing for different audiences and platforms.

  2. Soak Up Domain-Specific Knowledge: Once you've identified domain experts you respect, explore their work deeply. Read their books on copywriting techniques, follow their blogs on content strategy, and watch their presentations at marketing events. The more you engage with their specialized knowledge, the better you'll understand the field. Think of it as soaking up valuable insights that can enhance your understanding of the domain.

  3. Translate Domain Expertise into specific instructions: As you learn from these experts, pay attention to the techniques, patterns, and decision-making methods they use. Consider how you can convert these strategies into clear instructions for your AI system. For instance, you might develop prompts that help your AI follow the AIDA (Attention, Interest, Desire, Action) formula for writing marketing copy or guide it to vary sentence structures for improved readability. This process involves translating their expertise into actionable steps that your AI can use in the field of copywriting.

Implementation

Let's look at a practical example of how you can implement this approach. Say you've been studying experts who are great at simplifying complex ideas. You can use their techniques to improve your AI's ability to create more understandable content.

Here's a simple Python script that demonstrates this concept:

import os
from dotenv import load_dotenv
import google.generativeai as genai
from instructor import from_gemini, Mode
from pydantic import BaseModel

load_dotenv()

# Configure Gemini API
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Create Instructor-wrapped client
client = from_gemini(
    client=genai.GenerativeModel(
        model_name="gemini-2.0-flash-exp",
    ),
    mode=Mode.GEMINI_JSON
)

class SimplifiedContent(BaseModel):
    original: str
    simplified: str

def generate_simplified_content(prompt: str) -> SimplifiedContent:
    # Create enhanced prompt with new requirements
    enhanced_prompt = f"""
    Role: You're an expert at simplifying complex content. 
    Task: Simplify the given content for a layman audience.

    Instructions:

    1. Use clear and complete phrases to prevent any misinterpretation.
    2. Avoid using cringe or overused words; choose suitable synonyms instead.
    3. Write in simple terms with a casual, conversational style.
    4. Break down complex sentences into shorter, easy-to-read ones.
    5. Keep the original meaning intact while making the content more accessible.

    Content to Simplify:
    {prompt}
    """

    response = client.chat.completions.create(
        response_model=SimplifiedContent,
        messages=[{"role": "user", "content": enhanced_prompt}]
    )
    return response

# Example usage
complex_text = """
His first job as a minister in Washington, D.C. was short-lived because his abolitionist views clashed with those of his congregation.
"""

simplified = generate_simplified_content(complex_text)
print("Original:", simplified.original)
print("\nSimplified:", simplified.simplified)
When we run this script, we get:

Original: His first job as a minister in Washington, D.C. was short-lived because his abolitionist views clashed with those of his congregation.

Simplified: He didn't stay long at his first church job in Washington, D.C. This was because he strongly believed slavery should end, but the people in his church didn't agree with him.

This example shows how you can take expert knowledge (in this case, techniques for simplifying complex ideas) and turn it into instructions for your AI. The result is content that's easier for everyone to understand.

Conclusion

Learning from domain experts can significantly boost your development of AI products for specific fields. While having a principal domain expert on your team is the gold standard, this approach offers a valuable alternative when that's not possible. By tapping into domain-specific knowledge through research papers, industry blogs, and other specialized content, you can:

  1. Move faster in your development process for domain-specific AI products
  2. Produce better results by applying proven strategies from the target field
  3. Explore new possibilities you might not have considered without domain expertise
  4. Bridge the gap when you can't work directly with a domain expert
  5. Ensure your AI product aligns closely with the specific needs and practices of the target industry

The best part? You don't need a hefty budget to benefit from these domain expert insights. With just a bit of effort, you can use the wealth of specialized resources that experts share to improve your product and speed up your progress in that particular field. This method isn't meant to replace working with domain experts entirely, but it's an excellent way to enhance your domain-specific AI project when direct expert collaboration isn't an option.