The Definitive Guide to Synthetic Data Generation for LLMs in 2025
Large language models (LLMs) in 2025 are hungry beasts, devouring more text than the entire internet can provide. Enter synthetic data – artificially generated text and information – which has moved from a niche technique to the cornerstone of modern LLM training. In fact, today’s top-performing LLMs are trained on virtually all AI-generated content (on the order of 99% synthetic data) due to necessity and scale. Why this dramatic shift? Because we’ve simply run out of enough high-quality human text to feed cutting-edge models, and generating new data with AI is faster, cheaper, and in many cases more effective. This guide will explain what synthetic data is, why it now dominates LLM training, the risks (like mode collapse), and how to avoid pitfalls. By the end, you’ll see why synthetic data for LLMs is not just a buzzword but a foundational strategy – and how to approach it with confidence.
What is Synthetic Data for LLMs?
Synthetic data refers to data that’s artificially created rather than directly observed or hand-collected from the real world. In the context of LLMs, this usually means text (or code, dialogues, etc.) generated by an AI to be used as training material for another AI. It mimics the properties of real, human-produced data – for example, having the style and statistical patterns of natural language – but it doesn’t originate from an actual human writing or real-world events.
Crucially, synthetic data for LLMs can be produced in vast quantities on demand. An LLM can provide custom-tailored training data that might be impossible to collect from the wild. This approach has huge advantages: it sidesteps privacy issues (since no real personal data is used), and it fills gaps where genuine data is scarce or too labor-intensive to obtain. In short, synthetic data is machine-generated fuel for LLMs, created to supplement or even replace the human-generated text that LLMs traditionally learn from.
Why LLMs Rely on Synthetic Data in 2025
Not long ago, AI labs scraped forums, books, and websites to train LLMs – but by 2025, those wells are running dry. Synthetic data has become central to LLM training for a few key reasons:
Human Text is Tapped Out: As OpenAI’s former chief scientist Ilya Sutskever warned, the AI industry has hit “peak data,” exhausting the readily available human-written text. Elon Musk put it more bluntly: “We’ve now exhausted basically the cumulative sum of human knowledge… in AI training”. Many high-quality data sources are now gated or limited – over 35% of top websites block AI web scrapers, and an estimated 25% of previously accessible text data is no longer freely available. In short, there just isn’t enough new human data to satisfy giant models. Synthetic data offers a way out of this scarcity trap by generating fresh text in unlimited volume.
Cost and Scalability: Relying on humans to write or label text at scale is slow and expensive. Paying armies of annotators to create training datasets can run into millions of dollars. Synthetic data flips this equation. Once you have a base model, generating additional training text is relatively cheap and fast – essentially the cost of compute. A great case in point: the startup Writer created an LLM called Palmyra X 004 trained almost entirely on synthetic text, and it cost only about $700k to develop, versus an estimated $4.6 million if they had used traditional (human-sourced) data and methods. In 2024, Gartner analysts even predicted that 60% of all data used in AI projects would be synthetically generated. Scaling up LLMs via synthetic data is simply more efficient in terms of time and money.
Unlimited and Targeted Data Generation: With synthetic data, the sky’s the limit. Need more examples of a rare scenario? Just generate them. As one researcher quipped, “If ‘data is the new oil,’ synthetic data pitches itself as biofuel” – you can start with a small seed of real data and simulate an endless stream of new examples from it. This means LLM developers can craft specialized datasets on demand. For example, Meta augmented video training data by having an AI model (Llama 3) generate captions for videos, creating descriptions that would be hard to get via manual scraping. OpenAI even fine-tuned an update of GPT-4 using synthetic data to enable a new “Canvas” feature in ChatGPT. In domains where real data is scarce or sensitive (legal documents, medical dialogues, etc.), synthetic generation can produce representative, useful data without the real-world complications.
Widespread Adoption by AI Leaders: Perhaps the strongest testament to synthetic data’s importance is that every major AI lab is embracing it. OpenAI, Google, Meta, Anthropic – all are using synthetic data to train or fine-tune their flagship models. Anthropic has openly described using only AI-generated conversations to train Claude’s persona, with human oversight but no human-written prompts in that loop. Microsoft’s latest open-source model (Phi-4) was trained with a healthy dose of synthetic text as well. And in the open-source community, projects like DeepSeek-R1 have shown it’s possible to reach cutting-edge reasoning ability entirely via synthetic training. DeepSeek’s team had the model produce its own chain-of-thought solutions and then used the best AI-generated examples as new training data – basically teaching itself with synthetic reasoning chains. When everyone from small startups to frontier labs is using synthetic data, it’s a clear signal: the future of LLM development is largely synthetic.
In short, by 2025 synthetic data isn’t a last resort – it’s the norm. It addresses data scarcity, slashes costs, and opens new capabilities. That’s why today’s best LLMs are effectively built on synthetic data foundations, with human-written text playing a much smaller supporting role.
How to Avoid Mode Collapse: Best Practices for Synthetic Data
The good news is that AI practitioners have learned how to balance the synthetic data diet and keep models healthy. Here are some practical guardrails and best practices to use synthetic data effectively while avoiding its pitfalls:
Know what good data looks like: Completely ditching human-generated data is not advisable. Research shows that even mixing in a bit of real-world data helps mitigate the diversity loss from synthetic training. Think of real data as a reality anchor – it keeps the model’s knowledge aligned with the real world. The best synthetic data needs a "seed" of diverse, real-world data to start from. Leading AI teams employ a strategic approach: they begin with carefully curated real-world data, develop synthetic generation processes that faithfully capture these patterns, and then scale systematically. At Phinity, we've observed that even clients starting from zero successfully established robust synthetic data pipelines by initially investing in just 50-100 expertly labeled examples.
Thoroughly Curate and Filter the Synthetic Data: Quality control is paramount. Never assume that AI-generated data is automatically good. As one expert put it, “raw synthetic data isn’t to be trusted” without human review. You should review, filter, and deduplicate synthetic outputs just as you would real data. Remove obvious errors or garbage outputs. Ensure a diversity of examples. Ideally, involve human experts to spot subtle issues. In practice, this might mean using one model or humans to critique and grade the outputs of the generator model, and only keeping the top-quality examples (a strategy known as rejection sampling). The open-source DeepSeek-R1 model did exactly this – it generated solutions via RL and then selected the best 800k samples to use for further training. Curating synthetic data in this way prevents trash-in-trash-out and preserves diversity, acting as a safety net against collapse.
Maintain Diversity (Don’t Let the AI Self-Reinforce Too Much): One cause of mode collapse is a feedback loop where the model keeps seeing the same style of its own outputs. To counteract this, introduce diversity in your synthetic data generation. Use varied prompts and scenarios when generating data. Perhaps use multiple different models or iterations to produce synthetic data – e.g., ask both your current model and a different model to create training examples, or use different settings (temperatures, styles) so the outputs aren’t all cut from one cloth. The key is to make the synthetic dataset as rich and varied as a real dataset collected from many sources. Diversity of source = diversity of output.
Leverage Reward Modeling and RLHF: Many state-of-the-art LLM training pipelines incorporate reinforcement learning with human feedback (RLHF) or AI feedback to keep models in line. When generating synthetic data, you can similarly apply a reward model to score the outputs for correctness, helpfulness, etc. For example, OpenAI uses human-trained reward models to rank and improve ChatGPT’s outputs. In your synthetic data generation, consider using a reward model (which could even be your current LLM) to evaluate each generated sample. This helps in weeding out problematic or low-value content automatically. Reinforcement learning can also be used in a loop: have your model generate data, get rewards or critiques on it (from a model or human), and train the model to improve based on that feedback. Anthropic’s “Constitutional AI” is a variant of this idea – the AI is guided by a set of principles (a kind of AI-written constitution) to judge its own outputs, thereby creating safer synthetic data without constant human oversight. The takeaway: use AI feedback or human feedback to guide synthetic generation, so the data aligns with your quality and safety goals.
Monitor and Evaluate Continuously: Treat synthetic data training as an iterative process. Don’t just generate a huge dataset and blindly train for weeks. Instead, monitor your model’s performance on real-world validation sets at regular intervals. Keep an eye on signs of collapse: e.g., is perplexity on a holdout real dataset suddenly increasing (which could mean the model is deviating)? Are diversity metrics or output uniqueness dropping? Periodically evaluate the model on tasks that require real-world knowledge or creativity to ensure it hasn’t become too “robotic.” If you see warning signs, isolate the data that introduced the harmful behavior, generate data to reinforce good behavior, re-train, and course-correct. Synthetic pipelines are not fire-and-forget. With good monitoring, you can stop a collapse before it cascades.
By following these guardrails – blending real data, filtering for quality, ensuring diversity, using feedback mechanisms, and monitoring closely – practitioners have shown it’s possible to safely harness synthetic data at scale. For instance, teams at Meta and Anthropic incorporated some human feedback and periodic checks to successfully train large models with data that is 90%+ synthetic. And the open-source community’s experiments (like fine-tuning smaller models) found that carefully prepared synthetic data can dramatically boost performance without adverse effects. Synthetic data doesn’t have to mean sloppy data – with the right best practices, it can be as reliable, and even more performant than traditional data - cleaner, more diverse, and accessible.
The Road Ahead: Embrace Synthetic Data with Confidence
As we look forward, one thing is clear: synthetic data is here to stay in the world of LLMs. In fact, it will likely become even more dominant as models demand ever more knowledge. Sam Altman, OpenAI’s CEO, predicted that in the future AI will generate training data so good that it can “effectively train itself.” We’re not fully there yet – human oversight remains crucial – but we’re edging closer. The past year has proven that with careful management, synthetic data can dramatically accelerate AI development without destroying model quality. It’s no longer a secret that the latest GPT or Claude models behind the scenes rely heavily on AI-generated corpora. Even skeptical voices are coming around to accept that the only way to keep improving AI may be for AI to write its own curriculum.
While synthetic data generation presents challenges, organizations that master this technology gain a decisive competitive edge—developing superior AI systems at a fraction of traditional costs.
At Phinity, we recognize that synthetic data can be transformative when done right but potentially catastrophic when executed poorly. This dichotomy exists because the most effective techniques remain hidden in academic research papers and frontier lab environments.
Our approach bridges this gap by implementing proprietary methodologies derived directly from cutting-edge research—from diversity-aware generation techniques to AI-assisted filtration systems—ensuring our synthetic data meets the highest quality standards for specialized LLM applications.
By operating at the intersection of theoretical advancement and practical implementation, we deliver synthetic data solutions that are not merely faster and more cost-effective, but fundamentally more robust and credible than conventional approaches.
As LLM-driven synthetic data moves from experimental concept to essential technology, Phinity stands ready to help organizations transform this scientific potential into measurable business advantages.
Want a more technical overview of the current state of synthetic data in research? Check out our blog post here.