The Current State of LLM Synthetic Data: Research-Informed Guidelines for Generating Synthetic Data

May 7, 2025 - Phinity Labs

How do you generate synthetic data for LLM training? In this guide, we’ll take a research-backed look at synthetic data generation for LLMs – what it is, why it matters, and how it’s done – with insights from the latest academic research. We’ll cover the importance of data diversity and quality for model robustness, explore the key methods researchers use to generate and curate LLM-driven synthetic data, survey the current landscape (including notable findings and open challenges), and discuss how industry practitioners can apply these lessons. If you’re an executive, product manager, software engineer breaking into LLMs, or ML engineer skeptical about the hype, this post will ground you in the facts and equip you to make informed decisions about using synthetic data for LLM projects.

Large Language Models (LLMs) are hungry for data. Training or fine-tuning these models typically requires massive, high-quality datasets — often trillions of tokens for state-of-the-art systems (Chen et al.). Yet assembling such corpora from human sources is increasingly difficult due to cost, privacy limits, and inherent biases, and even carefully curated human data can carry errors or social prejudices, prompting the question: Can we find a more scalable, controllable way to feed LLMs the data they need?

Enter synthetic data for LLMs. Thanks to recent advances, modern LLMs (like GPT-4o and others) can generate text nearly indistinguishable from human writing. This means we can use the models themselves to create training and evaluation data on demand. In principle, an LLM augmented with the right prompts can produce thousands of diverse, instructive examples in minutes — examples that might have taken human annotators weeks to prepare. Moreover, LLM-generated data can be tailored to specific needs: models can be instructed to cover particular topics, styles, or edge cases, giving us fine-grained control over the dataset composition (Long et al.). These advantages make synthetic data a compelling alternative or supplement to human-collected data. Indeed, synthetic datasets are no longer just a research curiosity; by mid-2024, over 300 public datasets on Hugging Face’s hub are tagged as “synthetic”, and many open-source mainstream LLMs leverage high-quality synthetic data. For example, Stanford’s Alpaca and Vicuna were both built by fine-tuning base models on instruction-following data that was entirely generated by larger AI models. Alpaca’s 52,000 training examples were created using OpenAI’s text-davinci-003 model in a “self-instruct” style (training recipe), demonstrating how a well-crafted prompt and a strong teacher model can produce a rich training corpus with minimal human labor.

What is Synthetic Data for LLMs (and Why Now)?

Synthetic data refers to artificial data generated by a system (in our case, by an AI model) to mimic the characteristics of real-world data. In the context of LLMs, synthetic data usually means text (or code, etc.) produced by an LLM that can be used to train or evaluate models. For instance, instead of collecting thousands of real user queries and responses for a chatbot, one could prompt an LLM to imagine such dialogues and use those as training examples.

Why consider synthetic data now? The idea isn’t entirely new – simulation and data augmentation have been used in AI for years – but the game-changer is the quality of data that today’s large models can generate. Recent LLMs have ingested vast swaths of the internet and beyond, endowing them with extensive knowledge and fluent language ability. As a result, a well-prompted LLM can produce text that is often on par with human writing in coherence and relevance. This was not the case with smaller models from a few years ago. In essence, LLMs have reached a point where the model itself can be a data generator, not just a data consumer.

Moreover, synthetic data offers a solution to several pain points in the traditional data pipeline:

Scale and Speed: Once you have a capable LLM, generating millions of words of data is fast and scalable – only limited by computing power and the cost of API calls. This helps alleviate the quantity bottleneck for training data.
Cost-Effectiveness: Human annotation is expensive and slow. By contrast, letting an LLM write your dataset (perhaps with minimal human supervision for quality control) can be far cheaper. One survey notes that LLM-driven data generation can automate large parts of the training/evaluation pipeline with minimal human involvement.
Controllability: With synthetic generation, we can steer the data distribution. Need more examples of a rare edge case or a specific format? Simply prompt for them. LLMs allow us to design and balance the dataset in ways that would be impractical with found data. Researchers have highlighted that this ability to curate “what the models learn” via synthetic data is crucial for efficient training.
Ethics and Privacy: In scenarios where using real data is problematic (due to privacy laws or sensitive content), synthetic data can fill the gap. Since it’s machine-generated, it can be crafted to avoid personal identifiers or duplicated proprietary text, etc., sidestepping some legal concerns (though care must be taken that the model doesn’t inadvertently regurgitate training data).

None of this is to say synthetic data is a silver bullet or inherently superior to human data – but it expands the toolkit available to ML teams. It lets us supplement and enhance real datasets, as well as create entirely new ones in domains where real data is scarce. Given these benefits and the proven successes (like Alpaca, Vicuna, Llama 3, Nemotron, and many others in 2024), LLMs trained on 90%+ synthetic data became the norm. Researchers have used LLM-synthesized code and math problems to boost models’ coding and reasoning skills. Leading 2025 work confirms this shift: DeepSeek‑R1 trained itself using pure reinforcement learning and self‑generated data, yet matches o1 on code and math benchmarks, cutting training compute by 8×. Microsoft’s Phi‑4‑Reasoning (Apr 2025) demonstrates the teacher‑student loop at scale: a 14 B model fine‑tuned solely on DeepSeek‑R1‑generated demonstrations equals or beats models 5× larger on MATH and GSM8K. The community has also applied synthetic data to fine-tuning and instruction-tuning tasks (e.g. generating custom instruction-following datasets) with significant success, supported by the swath of synthetic datasets on Hugging Face.

The takeaway: Synthetic data for LLMs is here, and it’s working. Now, let’s delve into some key considerations – starting with one of the most critical: diversity.

Diversity: The Fuel for Robust Models

One of the biggest advantages of generating your own data is the ability to inject diversity. In machine learning, diversity means having a wide variety of examples that cover different contexts, styles, and edge cases. Academic studies stress that diversity in synthetic data is essential for training robust models (Long et al.). If your generated dataset is too narrow or repetitive, the model will likely overfit to those patterns or develop blind spots for scenarios it never saw. On the other hand, a diverse synthetic dataset – varying in topic, phrasing, length, tone, etc. – can mimic the richness of real data and help prevent overfitting and bias.

Think of it this way: real-world language is extremely varied. To generalize well, an LLM needs exposure to this variability. Synthetic data gives us a chance to ensure that exposure by design. We can ask for the same information to be expressed in multiple ways, or for unusual combinations of conditions, thereby covering the nooks and crannies of the input space that a random sampling of real data might miss.

Crucially, recent research backs up the link between diversity and model performance. For example, a 2024 study by Chen et al. introduced a new metric for quantifying synthetic dataset diversity and found that higher diversity scores correlate positively with downstream model accuracy. In controlled experiments, models trained on more diverse synthetic corpora outperformed those trained on less diverse data, even when the amount of data was the same (Chen et al.). Interestingly, they also observed that diversity in pre-training data had an even bigger impact on subsequent fine-tuning performance than on the pre-training itself, underscoring that a diverse foundation makes a model more adaptable when you specialize it to tasks later on.

However, achieving diversity is not as simple as flipping a switch. A naive approach might be to prompt an LLM with a high randomness setting (high temperature) to get varied outputs. Yet, studies have found that just cranking up randomness often isn’t enough – models can still produce highly repetitive outputs even with stochastic decoding. This is a known challenge: left to its own devices on a single prompt, an LLM might latch onto a pattern or mode in the data space, yielding lots of very similar samples. Diversity must often be engineered into the generation process.

Researchers have developed several techniques to encourage diversity in LLM-generated data:

Conditional Prompting: One effective strategy is to explicitly condition the generation on various desired attributes. Instead of a generic prompt like “Generate some training examples for task X,” one might say, “Generate a question about {topic} in a {formal} tone, aimed at a {beginner} audience.” By supplying different combinations of conditions (topic, style, difficulty, audience, etc.), you guide the model to cover a broad space. This method essentially injects structured variation into the prompt. Studies report that conditional prompting can automatically achieve an “artificially defined” diversity in the outputs. For example, one paper varied attributes like topic, length, and writing style and saw a significant boost in the diversity of generated texts by varying prompt attributes and combinations (Long et al.). By controlling the coverage of the data’s attributes, we avoid generating twenty clones of the same example and instead get a well-rounded dataset.
Prompting with Creative Randomness: Another approach is to introduce randomness in a more informed way. Eldan & Li (2023) describe adding “creative randomness” to prompts – for instance, appending a random twist or scenario to each request – which was shown to enhance output diversity beyond what a high temperature alone would do. The idea is to shake the model out of repeating formulas by giving it a slightly different context each time.
Iterative Generation & Refinement (what is best practice today for tasks like code SFT): Rather than generating all data in one shot, some workflows iteratively expand the dataset, aiming to hit new angles in each round. For example, the Self-Instruct method (discussed more later) starts with a few seed tasks and has the model generate new instructions based on those, then filters them and repeats, gradually broadening the pool of instructions (Wang et al.) . Other work suggests decomposing tasks or multi-step generation: break a complex generation task into parts or stages to ensure a wider exploration of the solution space. A recent technique called dataset-wise task decomposition dynamically adjusts generation conditions at each stage to maintain diversity as the dataset grows (Long et al. Section 3.1.2). The takeaway is that a smart generation process – possibly involving multiple passes or constraints – is key to producing a rich, non-redundant synthetic dataset. Variations on this like WizardLM Evol-Instruct, where you generate "mutations" of a prompt and use those mutations to recursively generate new prompts, and thus, new outputs, are now the norm in synthetic data generation for post-training.

By applying these methods, practitioners have managed to greatly improve the diversity of synthetic data. In practical terms, this means models trained on such data tend to be more robust – they handle unusual inputs better and are less likely to get stuck in a narrow mode of behavior. Diversity is a big part of why synthetic data, when done right, can match or even surpass the effectiveness of a similarly sized human-collected dataset. It’s not just about volume; it’s about covering as much of the task-relevant universe as possible.

Of course, diversity shouldn’t come at the cost of correctness or relevance – uselessly random data won’t help your model. The trick is balancing diversity with quality, which brings us to the next topic: how do we ensure the synthetic data is actually high-quality and useful?

How Do We Generate Synthetic Data? Key Methods and Workflows

Before talking about quality control, let’s overview how synthetic data is generated in the first place. Broadly, there are two paradigms for LLM-driven data generation:

1. Self-Generation (Self-Improvement): In this approach, we use the target model itself (or a weaker model) to generate new data, often in an iterative loop. The model “improves itself” by creating training examples based on its current knowledge, possibly refining them over multiple rounds.
2. Teacher-Student Generation (Distillation): Here, we use a stronger model (or ensemble of models) as a teacher to produce data for a smaller or less capable student model. The student then trains on this high-quality synthetic data, effectively distilling the teacher’s knowledge.

In practice, many workflows combine elements of both, but it’s a useful conceptual split. Let’s look at each and some concrete examples:

Self-Improvement via LLMs themselves. A landmark example of this is the Self-Instruct framework (Wang et al.). In Self-Instruct, researchers took a pretrained model (e.g. an earlier GPT-3) and asked it to generate its own instruction-following data. They began with a small set of human-written prompts and outputs (a “seed” set of tasks). The model was then prompted with these seeds to produce new instructions and responses, as if it were creating new Q&A or task demonstrations. Not all of these generations will be good, so the next step was critical: filtering out invalid or low-quality examples (more on filtering soon). The remaining high-quality synthetic Q&As were added to the training set, and the model was fine-tuned on this expanded dataset. This process can be repeated in cycles. Remarkably, Self-Instruct managed to substantially boost the model’s performance without any additional human-written data. The team reported a 33% absolute improvement on a benchmark of following novel instructions, bringing the self-tuned model nearly on par with a model that had been tuned on an expensive human-labeled dataset. In a manual evaluation of novel tasks, the Self-Instruct-tuned model’s outputs were almost as good as those of the human-tuned model, with only about a 5% gap remaining. This is a powerful proof-of-concept: a model can generate a large portion of the data needed to fine-tune itself.

Another self-improvement approach is self-play or iterative refinement. One recent method, Self-Play Fine-Tuning (SPIN), has a model engage in a sort of dialogue or game with itself to create new training data from its current mistakes (Chen et al.). Essentially, the model generates a query and its own answer, then assesses or “plays against” that answer to refine its policy, continuing this loop to sharpen its capabilities without new human examples. SPIN and similar techniques have shown that even a weak initial model can ascend to strong performance given enough self-generated data and iterative self-correction. The common thread in self-improvement methods is bootstrapping: use what the model does know to generate examples, filter or correct them, retrain, and repeat. Over time, the model’s knowledge and output quality broaden.

Distillation from a stronger model. If you have access to a very advanced frontier model, you can use it to generate data that a smaller model will learn from. This is like a teacher-student scenario. A prominent example here is the Stanford Alpaca project. The Alpaca team wanted to create an instruction-following version of a smaller open-source model (LLaMA-7B) that could approximate the behavior of OpenAI’s text-davinci-003. Instead of hiring crowdworkers to write thousands of instructions, they simply prompted text-davinci-003 itself to produce the instructions and ideal answers. They did this in a structured way (following the Self-Instruct recipe) to generate 52,000 diverse instruction-response pairs, and then fine-tuned LLaMA on this synthetically generated dataset (Alpaca Project). The result, Alpaca, performed surprisingly well — it exhibited many of the capabilities of the much larger teacher model. This approach essentially distilled the knowledge of the 175B-parameter GPT-3.5 model into a 7B-parameter student via synthetic data. Following Alpaca’s success (in early 2023), a flurry of other models used similar techniques: for instance, Vicuna was trained on user-shared ChatGPT conversations, another form of AI-generated data, and OpenChat and OpenAssistant gathered LLM-generated dialogues to fine-tune open models. Distillation lets you leverage the strengths of top-tier models (which may be too large, slow, or proprietary for your use case) by having them teach a smaller model through examples.

It’s worth noting that the line between self-generation and distillation can blur. Often, you might start with a teacher model to generate an initial dataset, then let the student model continue self-generating more data as it improves. Or you might have the student model generate data, but use a stronger model to filter or critique that data (a kind of hybrid). The overarching point is that LLM-driven synthetic data generation is a flexible, creative process. It can involve one or multiple models working in concert, possibly with some human oversight, to produce a dataset.

From a workflow perspective, a typical synthetic data generation pipeline includes steps like:

Prompt Design: Craft prompts or instructions that will get the model to output the kind of data you need. This might involve few-shot examples or role instructions (e.g., “You are a chatbot in a customer support role, generate a conversation about X…”). Research suggests giving the model clear task definitions and context (even a simple prologue like “Suppose you are a domain expert…”) helps produce more relevant and higher-fidelity data.
Generation Phase: Use the model (or models) to generate a large pool of candidate data. This could be done in one batch or iteratively. If using multiple conditions or prompts, ensure you cover the combinations you care about (as discussed under diversity).
Curation Phase: Filter and refine the generated data to ensure quality (we’ll dive into this next). Possibly augment or label the data if needed (for example, if you need category labels or difficulty ratings, you might generate those or add them via another model).
Integration: Take the curated synthetic data and use it for the intended purpose – e.g. add it to a training set or create a new benchmark for evaluation. Sometimes synthetic data is mixed with real data to get the best of both worlds.

Throughout this process, keeping an eye on diversity, correctness, and relevance is vital. A common mistake would be to assume the model’s job is done after step 2 (generation); in reality, a lot of the value comes from steps 1 and 3 – designing good prompts and rigorously curating the outputs. We’ll talk about curation next.

Quality Control: Curation and Filtering of LLM-Generated Data

If you’ve ever used an LLM, you know it’s not perfect. They can fabricate facts, produce unsafe content, or simply generate gibberish if prompted oddly. So when we rely on LLMs to create our dataset, quality control is paramount. The goal of the curation phase is to weed out the bad or noisy examples and end up with a clean, useful dataset for your model.

Researchers typically employ automated filtering, scoring, and editing techniques to curate synthetic data. Here are some of the approaches:

Heuristic Filters: The simplest filters are rule-based. For instance, one might remove any generated sentence that is too short, or that contains a certain unwanted phrase or placeholder. In synthetic dialogue data, you might filter out conversations that have the assistant responding with “I don’t know” or refusing to answer (if those are not useful for training). These rules depend on the task, but they act as a first pass to catch obvious duds.
Quality Scoring and Re-ranking: A more advanced approach is to have models themselves judge the outputs. For example, you can prompt a powerful LLM (or a specialized classifier) to rate each candidate sample on various criteria: correctness, coherence, relevance, etc. This practice leverages the fact that current top models are pretty good at evaluating text. In one notable study, researchers used GPT-4 to score a large set of synthetically generated instruction-following examples (the Alpaca dataset) and discard those with low scores. The filtered dataset – which they dubbed “Alpagasus” – had far fewer examples than the original, but those examples were of much higher quality. When they fine-tuned a model on Alpagasus (the curated data), it actually outperformed a model fine-tuned on the entire unfiltered Alpaca dataset. This result highlights an important point: for model training, data quality often trumps quantity. 5,000 excellent examples can beat 50,000 mixed-quality ones. By using an LLM as a knowledgeable curator (in this case, effectively having GPT-4 act as an automatic reviewer), we can dramatically boost the signal-to-noise ratio of synthetic data.
Consistency and Cross-Verification: Another filtering strategy is to check the consistency of the generated data. For instance, if you have an LLM generate a question-answer pair, you could feed the question back to a model and see if it produces the same answer. If not, the pair might be inconsistent or ambiguous and thus filtered out (Long et al. Section 3.2.1). Some works generate multiple answers or use multiple models and only keep examples where there’s agreement (the idea being that if two independent runs yield the same Q&A, it’s more likely to be correct). Techniques like this use redundancy to validate content.
Small Model Filters or Classifiers: Instead of using a big LLM for filtering, one can also train a smaller model or use existing classifiers to filter. For example, you might have a toxicity classifier and drop any synthetic examples that score high on toxicity, to avoid polluting your training set. Or use a fact-checker component to verify certain fields. An interesting direction is using smaller LMs in tandem with the big LLM: one paper (FreeAL by Xiao et al., 2023) showed that a lightweight model could learn to identify problematic synthetic samples (at much lower compute cost) and work with the large model to iteratively refine the dataset.

Beyond filtering out bad data, sometimes we also enhance labels or metadata for the generated data (the survey literature calls this label enhancement). For instance, if the synthetic data is unlabeled (just raw text), one might use the LLM or some heuristic to assign labels so it can be used for supervised training. This could be as simple as generating a summary or category for each output, or as involved as having humans spot-check and correct some of the model-generated answers (a little human in the loop).

After curation, how do we evaluate that our synthetic data is good? There are two angles: intrinsic evaluation of the data itself, and extrinsic evaluation by the performance of models trained on it.

Intrinsically, researchers have proposed various metrics to quantify properties of synthetic data. We discussed diversity metrics earlier. Other metrics look at data faithfulness or correctness (does a generated example stay true to known facts or the source context it was supposed to use?), and data coverage (how well the synthetic set covers the task distribution). For example, measuring diversity can be done by checking vocabulary richness or embedding-based similarity among samples. One might compute how many unique n-grams appear in the synthetic data versus a baseline, or cluster the data to see if there are many distinct groups. If all your synthetic examples cluster tightly together in embedding space, that’s a red flag for low diversity. For factual correctness, one might use QA pairs and verify answers against a knowledge source, or have an LLM judge if an answer is plausible. The LLM-driven diversity metric (Cluster-agent) mentioned earlier actually uses an LLM to help cluster and evaluate diversity (Chen et al.). The field is actively developing these metrics, because having automated ways to evaluate synthetic data is very useful for fast iteration.
Extrinsically, the ultimate test is: does using this synthetic data produce a better model or a better evaluation result? If you generated a synthetic test set, you might check if model rankings on that set agree with known rankings on real data. If you generated training data, you’ll see if adding it (or replacing human data with it) improves your model’s performance on a real validation set. Often, synthetic data is used to augment a small human dataset – in such cases we check how much the addition helps. A well-known finding is that carefully curated synthetic data can match the effectiveness of real data for tasks like instruction tuning, but this is task-dependent and requires that the synthetic data generation was done thoughtfully (as we have outlined). Many teams do a manual review of a sample of the synthetic data as well, just to sanity-check that it looks reasonable and varied.

In summary, curation is where a lot of the “magic” happens to ensure synthetic data is credible. Automated filters guided by heuristic rules or by powerful models can clean up the generations significantly. The process is analogous to hiring a team of editors to go through human-written data for errors – except here the editors can also be AI systems. And as a guiding principle from research: when in doubt, prioritize quality over quantity in your synthetic set. It’s often better to train on a smaller set of high-quality synthetic examples than on a massive dump of raw, unfiltered generations.

The Research Landscape: Findings and Open Challenges

The use of LLM-generated synthetic data is a young but rapidly evolving area. Dozens of papers in the past two years have explored different techniques, applications, and analyses. To give a quick overview of the landscape:

Key Findings So Far: We’ve already touched on many of these, but to recap:
- LLM synthetic data can effectively supplement or replace human data in many settings. Studies show models fine-tuned on purely synthetic instructions (from GPT-3/GPT-4) perform on par with those fine-tuned on human instructions. Synthetic pre-training corpora have enabled smaller LMs to reach abilities that previously required massive real text corpora. In data-scarce domains (like specific programming languages or niche medical topics), synthetic examples generated by a knowledgeable model have dramatically boosted performance by providing training signals that simply didn’t exist otherwise.
- Diversity and quality control are decisive factors. When synthetic data succeeds, it’s often because the creators managed to ensure it was broad and accurate enough. We saw how diversity correlates with better outcomes, and how filtering improves quality. Another paper observed that including long-tail or rare scenarios via synthetic data helped models generalize to those cases, whereas models trained only on human data failed on out-of-distribution tests.
- Combination with real data often yields the best results. Many practical pipelines use a mix of real and synthetic. For example, you might have a small seed of real data, use it to prompt an LLM to generate more, then merge them. The Phi-1 series models were trained on a blend of synthetic and real data and achieved excellent performance for their size (Gunasekar et al.). Synthetic data can be designed to fill the gaps in real data – providing examples that are rare or absent in the real set. This complementary use is very common in industry.
- Evaluation of LLMs via synthetic data is a growing area. Aside from training data, another big use of LLM-generated content is for creating evaluation benchmarks and test cases. For instance, to evaluate an LLM’s reasoning, one can generate a suite of tricky problems and their solutions using a stronger model, and then see how the target model does on those. This “AI-crafted exam” approach is being actively researched. It allows testing models on scenarios where obtaining loads of human-written evaluation data is hard. Early results are promising, but it’s important to validate that the synthetic test correlates with real-world performance (to avoid a model just doing well on an AI-invented test that doesn’t translate to reality).

Despite the progress, open challenges and frontiers remain. Current research is actively exploring these challenges:

Generating Truly Complex Data: While LLMs can spit out straightforward text easily, generating data that requires deep reasoning or multi-step problem solving is harder. For example, creating a dataset of detailed math word problems with correct solutions is non-trivial – the model might make reasoning errors. Complex tasks often require the model to plan or use tools (e.g. a calculator), which single-turn prompts don’t handle well. One future direction is breaking down complex data generation into sub-tasks (decomposition) or employing LLM “agents” that can use external tools during generation. Research in 2024 has pointed toward developing data generation agents – systems that orchestrate an LLM through multi-step workflows to produce complex synthetic data (imagine an autonomous script that can generate a physics problem, call a solver to get the answer, verify it, and package it into a training example). This is an active area and we expect more progress on complex, multi-turn synthetic data generation.
Incorporating Knowledge and Factuality: LLMs sometimes generate incorrect or shallow content, especially in specialized domains. If an LLM doesn’t know much about, say, veterinary medicine, any synthetic data it creates on that topic might be full of errors or clichés. Researchers are looking at ways to inject real domain knowledge into the synthetic data process. One approach is to connect the LLM with external knowledge bases or retrieval systems during generation. For instance, an LLM could be prompted to generate a question, then actually query a database for the answer, and incorporate that into the synthetic example – ensuring factual correctness. Another idea is to use domain-specific language models (smaller ones fine-tuned on the domain) in the loop to fact-check or guide the main LLM. The survey by Long et al. (2024) suggests that knowledge-enhanced generation – leveraging knowledge graphs, web searches, or domain experts – will be key to producing high-quality synthetic data in fields where the base LLM might be weak. The challenge is to do this systematically and scalably.
Balancing Large and Small Models (Efficiency): Using GPT-4o to generate every piece of data is effective but extremely costly. A growing line of work examines how smaller, cheaper models can assist or take over parts of the generation pipeline without sacrificing much quality. We mentioned the idea of using a small model to filter data (FreeAL). Another idea is to have a small model generate draft data and a large model just polish or verify it, which could cut compute costs. The collaboration between models – perhaps a fleet of specialized generators and validators – is a frontier being explored. If done right, it could democratize synthetic data generation (so you don’t need an API budget of millions to build a dataset).
Human-Model Collaboration Frameworks: Last but certainly not least, there’s the question of how to involve humans in the loop efficiently. Fully automated synthetic data generation is powerful, but it runs the risk of compounding errors or introducing subtle biases that the model doesn’t realize. Humans are very good at spotting these issues, but you obviously can’t have a person check a million examples one by one. The challenge is to inject just enough human oversight to steer the process, without negating the scalability gains. Some research has proposed interactive interfaces where humans can guide the data generation strategy (e.g., by specifying areas where the model should generate more data, or by quickly rating samples which the model then learns from). So far, there isn’t a standardized framework for human-in-the-loop data generation. What’s clear is that a bit of human intervention – even if only to audit a sample of the data or to correct the model when it starts veering off – can prevent the worst failures (like model-generated data containing toxic misinformation that looks fine to an AI but a human would catch instantly). Long et al. note that without any human oversight, models can inadvertently produce biased or toxic data that “poisons” the training process. Thus, figuring out the right workflow for human–AI collaboration in synthetic data creation is an open problem. Likely, future pipelines will involve periodic human evaluation checkpoints or a final human review of a subset of data, especially for high-stakes domains.

All these challenges represent active research questions in 2025. The good news for practitioners is that the awareness of these issues means new tools and methods are emerging to tackle them. For example, expect to see libraries that come with built-in filters for LLM-generated text, or interfaces that let you plug in a knowledge base for the model to draw from as it generates data. The academic community is rapidly iterating on what works and what doesn’t, moving toward more reliable and systematic synthetic data generation.

Conclusion

Synthetic data generation for LLMs has evolved from a novelty into a reliable, even indispensable, tool in the deep learning toolbox. By leveraging the very capabilities of large models to create training and test data, we unlock a level of flexibility and scale that was previously out of reach. The 2024–2025 research shows that with careful attention to diversity, quality control, and the clever orchestration of models (and humans), we can produce datasets that rival traditional human-curated ones in effectiveness. For skeptics rightfully asking “Can AI-generated data really be trusted?”, the evidence is increasingly saying yes – if done with diligence. We’ve seen smaller models leap in performance thanks to synthetic instruction tuning, and we’ve seen how pitfalls like bias and repetition can be mitigated through thoughtful prompt and filter design.

For CTOs and ML engineers, the message is: don’t ignore this trend. Synthetic data for LLMs isn’t just academic tinkering; it’s being used in industry to train dialogue agents, to generate test suites for model evaluation, and to adapt models to new domains quickly. It’s a practical solution to the data bottleneck that often slows down AI projects. Of course, it requires know-how – one must apply the best practices from research to get good results (as we’ve outlined, things like maintaining diversity, employing a review process, etc.). But the barrier to entry is lower than ever, thanks to open-source tools and the collective lessons learned from recent studies.

As the field progresses, we expect synthetic data generation to become more automated and robust. Imagine a future where you can simply specify, “I need a dataset of 100k QA pairs about European history at varying difficulty levels,” and an AI system will generate it, verify it with external sources, ensure a spectrum of question types, and hand it to you ready to train — all in a matter of hours. We’re not quite there yet, but the trajectory points in that direction.

At Phinity, we've developed a synthetic data generation and curation engine that surpassed Evol-Instruct on diversity and performance for domain-specific tasks like biomedical reasoning and hardware design. We've also surpassed real data baselines. Read more about our work here.