Everything you need to know about Synthetic Data Generation

Synthetic data is artificial data that looks and behaves like your real data. It is created using algorithms and models to copy the patterns and trends found in actual data. Even though it is based on real information, it does not include any actual value from original dataset.

Why would you want fake data? Sometimes, working with real data can be tricky. It might be private, limited or expensive to collect. Synthetic data gives you way to test systems, train AI models and analyze trends without worrying about privacy or access issues.

And you can create as much synthetic data as you need and you can customize it to have different traits. Which makes it super useful for training machine learning models. If you do not have enough real data or it is tough to get, synthetic data gives you a way to fill in the gaps. It helps you build smarter AI without waiting for real-world data to show up.

What is synthetic data generation

To reiterate, think of synthetic data generation as making a realistic looking copy of real data without actually using any real information. Instead of collecting data from real events, you create it using algorithms, models or simulations.

The idea is to keep the same patterns and insights as real data while skipping the privacy risks, security concerns and the headache of gathering tons of real world information. It’s a smart way to work with data without all the issues that comes with the real thing. And you don’t have to risk sharing sensitive or personal details with fake data. It’s a safe option for data analysis, research, and software testing.

These are the synthetic data generation methods that you should know about

There are different ways to create synthetic data, depending on what you need it for. Some methods are simple and rule based while others use advanced AI techniques to generate highly realistic data.

Traditional methods

Random sampling allows you to pick values from set range making it so easy to create data but it may not always capture real world patterns accurately.

Conditional data generation lets you define specific rule based on business logic which works well for focused tasks but is not ideal for large datasets.

Data shuffling mixes up real data to create a new dataset while keeping overall trends intact. But you need to be careful. Because if not done right it can still pose privacy risks.

Data augmentation is useful when working with images or text. You can do tweaks to existing data by flipping a image, changing colors or replacing the words to add variety and improve AI training.

Statistical modeling

Uses math and probability to help you create synthetic data that mimics real world trends. One popular approach is the Monte Carlo method. Where you use random sampling and statistics to predict different outcomes. Which helps you generates data that follows real life patterns, making it useful for simulations, risk analysis and decision making.

Machine learning & AI methods

Advanced AI techniques can generate highly realistic synthetic data. Variational autoencoders (VAEs) learn from real data, compress it in to simpler version and then generate new data with similar patterns. They are great for creating synthetic text and images.

Generative adversarial networks (GANs) work by having two AI models - one creates fake data and the other checks if it looks real. Over time the generator improves, producing highly realistic data.

Simulation based Data

Creates synthetic data by simulating real world events. It is useful for studying complex system like traffic patterns, healthcare operation and financial markets. Instead of using real world records, a simulation models how things work and generate data based on those interactions.

Hybrid approach

And of course we have the hybrid technique. Combines real and synthetic data. If you have only part of a real data set, models can fill in missing pieces with synthetic data. Which is useful approach when real data is limited but still needed for training AI models or running analysis.

Each of these methods has its advantages. The right one for you depends on your industry, the type of data you need and how much accuracy matters for your use case.

Creating artificial data using synthetic data generation LLM

While there are many synthetic data generation methods available to generate synthetic data, building a large dataset from scratch can be time consuming, expensive and frustrating. You have to collect real data, clean it up and label everything.

Why not use LLMs to create the data you need with out all the manual work. They can create data more detailed than human labeled ones. So that you can test and improve AI models faster. All while making sure your data covers a wide range of scenarios. You can use this data to train, fine tune or even test other LLMs.

Let us give you a quick overview of how you can generate synthetic data with LLMs

1. Break documents into smaller parts (chunking)

Start by splitting large documents into smaller, meaningful pieces. Which makes the data easier to manage and keeps it organized. You can use different methods to do this, like such as cutting text by sentence length or meaning.

2. Group similar pieces (context generation)

Once you have broken down the document, you need to group related pieces together. Why? Because it will ensures that the synthetic queries you create later make sense. You can use techniques like clustering or cosine similarity (which measures how similar two pieces of text are) to do this.

3. Generate synthetic questions (query generation)

Now it’s time to use an LLM to generate questions or prompts based on the grouped text. To get high quality queries, give the LLM clear instructions so it creates a mix of simple and complex question.

4. Make queries more complex (data evolution)

To guarantee generated queries are diverse and challenging, you can modify them using templates. You can add follow up questions, hypothetical scenarios or multi step reasonings. All this makes the dataset more useful for training AI models.

5. Create ideal answers (optional)

If needed you can also generate the perfect answers for your synthetic questions using the same LLM. These answers act as a reference for testing AI performance. You can even review and tweak them manually to ensure accuracy.

6. Clean and format data (filtering & styling)

Before finalizing your dataset, remove any low quality or irrelevant queries. Make sure the data is formatted in a way that fit your needs whether it is converting text into SQL, JSON or another format.

By following these steps, you can quickly generate high quality synthetic data with LLMs.

About Floatbot.AI

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We are trusted by leading organizations across insurance, collections, lending, healthcare and banking for their ongoing needs. Whether you want to provide self-service agents, automate calls or complex workflows, we have solutions that seamlessly integrate into your workflow. Our platform is scalable, secure and affordable.