The One Technique You Need To Sharpen and Maximize Your Large Language Model

Large Language Models are just that - large. Like much of our work with others, LLMs require a bit of mentoring and coaching. To draw out the best it has to give, we have to train it to think like the rest of our team thinks.

As a leadership coach, I teach people how to think, not necessarily what to think. Fail to do this, and team members will face situations where they ask you what to do, rather than be able to decide what is best to do without having to come to you.

You need independent actors who are trained in the ideas and ways you do things. Welcome to working with the Large Language Model (LLM).

I have written before: coaches draw out, mentors pour in. We need both.

When you face a Large Language Model, you are a coach and a mentor. The most effective coaching is driven by asking the best questions. You are drawing out of the model the best information or course of action. You mentor it by training it how to give you that information.

Given that Generative AI will create 10% of all data generated by this time next year, it is crucial to teach the model you use to think in the way you need it to think.

Teaching The Big Brain

For organizations that strive for efficiency and agility, understanding “Few-shot learning” is a game-changer.

What is Few-shot Learning?

Traditional machine learning models often require vast amounts of data to be trained effectively. However, few-shot learning enables these models to understand and make accurate predictions or take actions with very limited data, mimicking the human ability to learn from few examples.

For instance, I may train an LLM to think and act a certain way by giving it a couple examples to follow:

Task: Convert descriptions into recipes.

Example: Description: A chocolate cake with a cherry on top.?

Recipe:

• 1. Mix chocolate batter.
• 2. Pour into a cake pan and bake for 30 minutes.
• 3. Once cooled, top with a cherry.

After giving it a similar example or two, I would give it a prompt: “Based on the examples, convert the following description into a recipe: Description: A vanilla milkshake with whipped cream and a cherry. Your recipe: ________”

It will give you a fine recipe (as it did here):

• 1. Pour 2 cups of vanilla ice cream, 1 cup of milk, and 1 tsp of vanilla extract into a blender.
• 2. Blend until smooth and creamy.
• 3. Pour the milkshake into a tall glass.
• 4. Top with whipped cream.
• 5. Place a cherry on top of the whipped cream.
• 6. Serve immediately and enjoy!

You train a model to think the way you need it to think.

Why is it relevant for CIOs?

Few-shot learning sharpens and leverages your LLM in multiple ways:

• Reduced Data Dependency: Many organizations struggle with limited data, especially when entering new markets or domains. Few-shot learning can help you kick-start AI initiatives without waiting for large datasets.
• Rapid Prototyping: Speed up the process of deploying AI models by reducing the need for extensive training.
• Cost-Efficiency: Less data means reduced data acquisition, storage, and processing costs.

3. How to Best Use the Few-shot Technique?

• Start with a Pre-trained Model: Few-shot learning is most effective when you start with a model that's already been trained on a vast amount of data. This model has generalized knowledge which can be fine-tuned with limited data.
• Select Relevant Data: Focus on obtaining or curating the most representative and diverse examples for your specific task.
• Iterative Feedback: Like any AI model, a few-shot learning model improves with feedback. An iterative approach where the model is regularly tuned based on outcomes can enhance accuracy.
• Combine Traditional and Few-shot Techniques: In cases where you have a moderate amount of data, combining traditional techniques with few-shot learning can yield improved results.

4. Potential Applications for Enterprises:

• Customer Support: Quickly adapt chatbots or support systems to handle new products or services.
• Fraud Detection: Swiftly adapt to new fraudulent techniques without waiting for large datasets.
• Personalization: Tailor user experiences in new markets without extensive data collection.

5. Risks and Considerations:

• Overfitting: Since there's limited data, there's a risk that the model could overfit to it. Regular validation and testing are essential.
• Data Quality: A few misrepresentative examples can skew the model's understanding. Ensuring data quality becomes more crucial than ever.

6. Looking Forward:

Few-shot learning is a step towards making AI more adaptable and human-like in its learning capabilities. It's not just a technical tool; it's a strategic enabler. As CIOs, harnessing its power effectively can give your organizations a competitive edge, ensuring that your AI investments are agile, efficient, and responsive to ever-evolving business needs.

Hang around me long enough, and you will hear me use the word “customize.” It’s a value that drives everything we do at CIO Mastermind.?

You need to customize your LLM. If you want it to give you game-changing results, mentor and coach it. The Few-Shot technique is the first practice to perfect.