Synthetic data generation

For automatically generating a large, diverse synthetic prompt dataset to test an LLM, I’d start with a synthetic data generation framework that supports controllable diversity, automated evaluation, and repeatable generation pipelines rather than a simple prompt-writing tool.

The key distinction is whether you want to generate lots of different questions or build a dataset that can actually expose weaknesses in your model. The strongest setup varies topics, task types, difficulty, user intent, wording, edge cases, and expected behaviors — while filtering out duplicates and low-quality examples.

My top recommendations

1. DeepEval

(Best starting point)

Best if you want to generate a large, varied evaluation dataset programmatically. Its synthetic-data features support generating test cases for LLM applications, including RAG-style scenarios, with configurable generation and evaluation workflows.deepeval.com+3

Good for: Python workflows, repeatable dataset creation, quality metrics.

Documentation: Synthetic data generation

2. Promptfoo

Best if your goal is to stress-test a model with lots of diverse and adversarial inputs. It is particularly useful for red teaming, prompt injection testing, comparing models, and running automated tests in CI.genai.qa+1

Good for: security testing, regression testing, cross-model comparisons.

Official website

3. Giskard

A candidate if you want model-quality and vulnerability testing workflows rather than just a raw collection of generated prompts. It is worth considering when you need to discover failure modes and turn them into useful tests.

Good for: model risk, robustness, and quality assurance.

Official website

4. SynSmith

A more specialized option for attribute-controlled, diverse synthetic data. Its approach uses iterative critic-guided refinement to address repetitive or low-quality generated examples. It is a research-oriented framework, so expect more experimentation than with a general evaluation tool.github.com+2

Good for: research, fine-grained control over dataset characteristics.

GitHub repository

Which one should you choose?

Your primary goal	Best fit
Generate thousands of synthetic evaluation cases	DeepEval
Generate adversarial prompts and security tests	Promptfoo
Experiment with controlled diversity and critic feedback	SynSmith
Evaluate RAG systems	DeepEval, with as another option

One important caveat: these tools solve different parts of the problem. DeepEval is the closest match for synthetic dataset generation; Promptfoo is more of a testing and red-teaming harness.deepeval.com+2

How I would build the dataset

For a genuinely large and diverse dataset, I'd use a pipeline that:

1. Defines categories such as reasoning, coding, summarization, instruction following, ambiguity, multi-turn dialogue, and safety.
2. Generates prompts with controlled variations in difficulty, length, style, domain, and user intent.
3. Generates edge cases and adversarial variants, not just paraphrases.
4. Deduplicates prompts using exact matching and semantic similarity.
5. Scores coverage and quality, then samples a balanced dataset.
6. Keeps a human-reviewed benchmark separate from the synthetic data used for exploration.

Synthetic examples are most useful when they supplement real user requests and curated test cases, rather than replace them entirely.deepeval.com+3

One question to narrow down the best tool: Are you generating prompts to evaluate your model's general capabilities, to red-team it for jailbreaks and safety failures, or to test a specialized application such as RAG or an agent? That distinction could change my recommendation.

Yes. My top recommendation is Promptfoo if your goal is specifically to generate a large, varied test set for an LLM and then immediately evaluate the model against it.

Best options

Tool	Best for	Synthetic prompt generation	Evaluation
Promptfoo	Broad LLM testing + adversarial cases	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
DeepEval	Python-first evaluation + synthetic datasets	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Giskard	ML/LLM testing with a more platform-oriented workflow	⭐⭐⭐⭐	⭐⭐⭐⭐

Promptfoo has a particularly useful generate dataset capability: you can give it a few seed prompts/test cases and instructions such as “generate diverse edge cases, different user personas, ambiguous requests, and adversarial inputs.” It can generate unique test cases and control the number of personas and cases per persona.www.promptfoo.dev

It also has a separate red-team generator that can produce hundreds of adversarial inputs across different harm categories, which is valuable if you want your test set to go beyond ordinary paraphrases.github.com

Promptfoo dataset-generation documentation

If you're Python-heavy: DeepEval

DeepEval is probably the alternative I'd choose. Its Synthesizer can generate thousands of synthetic goldens, including variations based on your knowledge base and different complexity/evolution strategies.deepeval.com

One important caveat: DeepEval explicitly recommends treating synthetic data as a supplement to real/production examples rather than blindly generating huge datasets and assuming they're representative.deepeval.com

What I'd use for your situation

If you're trying to build something like:

50 seed prompts → 10,000 diverse test prompts → run against my LLM → identify failure modes

I'd use Promptfoo and structure generation around dimensions such as:

• User personas: novice, expert, frustrated, terse, verbose, etc.
• Intent variations: direct, indirect, ambiguous, multi-intent
• Difficulty: easy → pathological edge cases
• Input style: typos, slang, incomplete sentences, multilingual
• Context: missing, excessive, contradictory, irrelevant
• Adversarial: prompt injection, jailbreak attempts, instruction conflicts
• Domain edge cases: rare but legitimate requests
• Expected behavior: answer, clarify, refuse, escalate, or use a tool

That gives you controlled diversity rather than simply asking an LLM to “make 10,000 more prompts,” which tends to produce lots of near-duplicates.

If you tell me what kind of LLM you're testing (chatbot, RAG, agent, classifier, coding model, etc.), I can also recommend a concrete synthetic-data pipeline and give you a starter generator configuration.

For LLM evaluation datasets, the “best” synthetic data generation tool depends less on generic synthetic data capability and more on whether you need RAG evals, agent simulations, instruction-following tests, safety tests, or domain-specific benchmarks. Traditional synthetic-data platforms (for tables/PII/test databases) are often not the right fit.GitHub+1

A practical shortlist:

Tool	Best for	Strengths	Tradeoffs
DeepEval Synthesizer	General LLM eval sets	Generates “goldens” (input + expected output + context), supports evolution of difficulty, integrates directly with eval runs	Best when paired with human review; synthetic-only sets can miss real failuresDeepEval
Ragas testset generation	RAG applications	Generates QA pairs from documents and evaluates retrieval + generation quality	Primarily RAG-focusedGitHub
Argilla + LLM generation pipelines	Human-in-the-loop datasets	Strong annotation workflow and dataset management	Requires more assembly
NVIDIA NeMo data tooling	Large-scale synthetic corpora	Good for scalable generation pipelines, including specialized tasks	More infrastructure-heavyGitHub
LangSmith dataset generation	LangChain/LangGraph apps	Easy path from production traces → datasets → regression tests	Best inside that ecosystemFuture AGI
Promptfoo	Prompt/model regression testing	Lightweight, CI-friendly eval workflow	Synthetic generation is not its main strengthFuture AGI

My default recommendation

For most teams building an LLM evaluation suite:

1. Start with production data or curated examples

• Export real user prompts, failures, support tickets, agent traces, etc.
• Human-review a representative subset.
• Use synthetic generation to expand coverage, not replace reality.DeepEval

2. Use an LLM eval-specific generator

• For general chat models: DeepEval Synthesizer
• For RAG: Ragas
• For agent workflows: LangSmith dataset generation or an agent simulation framework
• For large custom benchmarks: distilabel or custom pipelinesGitHub

3. Add automated quality filters A good synthetic generation pipeline should:

• generate examples from a taxonomy of failure modes
• use a judge model to score quality
• remove duplicates
• enforce difficulty buckets
• track provenance (which document/example produced each case)
• periodically compare against real failures

A strong architecture looks like:

Production traces
        |
        v
Data cleaning + clustering
        |
        +----------------+
        |                |
        v                v
Human-reviewed seed   Synthetic expansion
        |                |
        +-------+--------+
                |
                v
        Eval dataset registry
                |
                v
        Model regression suite

If I were building a serious internal LLM eval benchmark today, I would usually choose:

• DeepEval or Ragas for generation + evaluation logic
• Argilla for annotation/review
• Promptfoo/LangSmith/CI integration for running regressions
• A custom LLM-based generator for domain-specific edge cases

The biggest mistake is optimizing for “millions of synthetic examples.” A smaller set of high-quality, failure-oriented eval cases usually provides much more signal.DeepEval