Creating Effective Gen-AI Solutions

In today's data-driven world, creating effective AI solutions requires a clear understanding of business challenges and a strategic approach to problem-solving. Here’s a simple guide to help you navigate this process.

1. Define the Business Challenge Clearly

The first step in creating a meaningful Gen-AI solution is to clearly define the business challenge. Often, problems are not well-defined, making it hard to find the right solution. Your initial task is to clarify the challenge to ensure you are addressing the right problem.

Example:

• Unclear Challenge:?"We need to improve our customer service."
• Clear Challenge:?"We need to reduce the average response time of our customer service team from 24 hours to 2 hours by implementing an AI-powered chatbot."

2. Translate Business Challenges into Technical Challenges

Once the business challenge is defined, translate it into a technical challenge. This involves figuring out how to solve, manage, and deliver the solution. This task is typically done by data scientists, often in collaboration with Subject Matter Experts (SMEs).

Example:

• Business Challenge:?"We need to reduce the average response time of our customer service team from 24 hours to 2 hours."
• Technical Challenge:?"Develop an AI chatbot that can handle 80% of customer inquiries, reducing the workload on human agents."

3. Consider Review Costs and Accuracy

Gen-AI solution is only useful if the review costs are manageable and the accuracy is acceptable. For example, reviewing entire legal documents can be costly. When creating insights from a legal document, cross-verifying the accuracy can be time-consuming and expensive. The AI solution should assist but not replace human review entirely.

Example:

• High Review Cost:?Manually reviewing every customer service interaction.
• Balanced Approach:?Using an AI chatbot to handle common inquiries and human agents to review complex cases.

4. Define Success Criteria and Performance Metrics

Success criteria can vary, but generally, you should consider:

• Cost of the solution
• Execution speed
• Tokens per minute (TPM) and requests per minute (RPM)

Explanation:

• Tokens:?In AI, a token is a unit of text, such as a word or a character.
• TPM and RPM:?These metrics measure how many tokens or requests the AI can process per minute, indicating its efficiency.

Example:

• Success Criteria:?Reduce response time to 2 hours, maintain customer satisfaction score above 90%, and handle 1000 requests per minute.

5. Validate Feasibility

To avoid confusion about whether a solution is feasible, ensure the objective of the language model aligns with your business case. The primary function of a language model is to generate the next word based on previous words. If this principle fits your business case, the solution is likely feasible.

Example:

• Feasible:?Using a language model to generate automated responses for customer inquiries.
• Not Feasible:?Using a language model to predict stock market trends without historical data.

6. Optimize Based on Volume

If your data volume is minimal, prioritize going to market quickly without extensive optimization. For high-volume cases, take the time to build a robust, optimized solution before production.

Example:

• Low Volume:?A small startup with 100 customer inquiries per day.
• High Volume:?A large corporation with 10,000 customer inquiries per day.

"The best way to predict the future is to invent it." – Alan Kay

7. Communicate with Stakeholders

Simplify and explain the solution to business stakeholders in their language. Clear communication ensures everyone understands how the solution works and its benefits. This task is typically done by data scientists and project managers.

Example:

• Simplified Explanation:?"Our AI chatbot will handle common customer questions, freeing up our team to focus on more complex issues."
• Detailed one :"Our AI chatbot works by taking the input query from a customer and understanding the context of the question. It uses advanced language models to generate a relevant and accurate response. This process involves analyzing the words and phrases in the query, predicting the most appropriate answer based on previous interactions, and delivering the response in real-time. By handling common customer questions, the chatbot frees up our team to focus on more complex issues, improving overall efficiency and customer satisfaction."

8. Choose the Best Method

Different challenges may have multiple solutions. Your clarity in defining the problem will help you select the best method. Use cross-validation and predefined metrics to guide your decision. This is a collaborative effort between data scientists and SMEs.

Example:

• Single Record Processing:?Handling one customer inquiry at a time.
• Batch Processing:?Handling multiple customer inquiries simultaneously.

9. Handle Unlabeled Data

Large datasets without labels can be challenging. Start with a subset of labeled data, then have SMEs review and certify the labels. Use this labeled data to label the remaining data. This iterative process helps in deciding whether to use prompt engineering, fine-tuning a language model, or a simpler model.

Example:

• Unlabeled Data:?A dataset of customer feedback without categories.
• Labeled Data:?Using AI to categorize feedback as positive, negative, or neutral, then having experts review it.

10. Plan for Optimization

Start with a large language model if necessary, but plan to optimize over time. Record all inputs and outputs to develop a simpler, more robust machine learning solution. This process involves data scientists and SMEs.

Example:

• Initial Solution:?Using a large language model to handle customer inquiries.
• Optimized Solution:?Developing a simpler, more cost-effective model based on recorded data.

11. Utilize Batch API for Cost Efficiency

Learn to use OpenAI's Batch API for asynchronous requests, which can lower costs by 50% and offer higher rate limits. This is ideal for jobs that don't require immediate responses. Example:

• Immediate Response Needed:?Real-time customer support.
• Batch Processing:?Analyzing customer feedback overnight.

12. Continuous Improvement and Monitoring

AI solutions require continuous improvement and monitoring to ensure they remain effective and relevant. Regularly review performance metrics, gather feedback, and make necessary adjustments. This is a collaborative effort involving data scientists, SMEs, and business stakeholders.

Example:

• Continuous Monitoring:?Regularly checking the AI chatbot's performance and making updates based on user feedback.