Synthetic data generation

For automatically generating a large, diverse dataset of prompts to test an LLM, I’d prioritize tools that support programmatic generation, controllable diversity, and automated evaluation—rather than tools that simply generate lots of paraphrases.

The best choice depends on whether you want general-purpose test prompts, realistic user conversations, adversarial edge cases, or domain-specific test data. Ideally, your tool should let you generate thousands of examples, vary difficulty and intent, and measure coverage so you can identify gaps in your test set.

My top recommendations

1. DeepEval

(Best overall for synthetic test datasets)

DeepEval's Synthesizer generates test examples from scratch, existing examples, or documents. It can evolve examples to different complexity levels, create expected outputs, and simulate multi-turn conversations. This makes it a strong default when you want a large, diverse evaluation dataset rather than just a pile of raw prompts.deepeval.com+1

Best for: Python workflows, regression testing, RAG, and conversational applications.

Official documentation ·GitHub

2. Promptfoo

Best for fast, CLI-driven test generation

Promptfoo can extend existing test datasets with new, unique test cases using instructions you specify. It's particularly useful if you want to generate edge cases, then run the resulting dataset against multiple models or prompts.www.promptfoo.dev+1

Dataset generation docs ·GitHub

3. Ragas

Best for RAG-focused synthetic data

Consider Ragas if your test prompts should exercise retrieval, context relevance, grounded answers, and other RAG-specific behavior. It's a useful specialist to compare with DeepEval when your system is primarily retrieval-augmented generation.

Official website

These recommendations reflect feature fit, not a controlled benchmark of generation quality or cost.

Which should you choose?

Your main goal	Best fit
Generate thousands of diverse general-purpose test cases	DeepEval
Generate adversarial prompts, jailbreaks, and injection tests	Promptfoowww.promptfoo.dev+1
Generate tests grounded in your RAG documents	DeepEval or Ragasdeepeval.com+1

How I'd build the dataset

I'd use DeepEval as the primary generator and structure the dataset around several independent dimensions:

• Task: reasoning, summarization, extraction, coding, classification, instruction following.
• Difficulty: straightforward, ambiguous, multi-step, conflicting constraints.
• Input variation: typos, different phrasing, long context, multilingual inputs.
• Behavioral edge cases: insufficient information, false premises, conflicting instructions, unanswerable questions.
• Conversation structure: single-turn prompts, follow-up questions, changing requirements.
• Expected behavior: correct answer, appropriate refusal, clarification, or explicit uncertainty.

Generate an initial batch, remove near-duplicates, check coverage across categories, and manually review a representative sample. Keep a separate held-out set for measuring performance. Synthetic data can improve coverage, but automatically generated examples can also share biases or contain incorrect expected answers. DeepEval's own guidance recommends grounding examples in documents or reviewed examples where possible.deepeval.com+1

My verdict: Start with DeepEval's Golden Synthesizer for broad synthetic dataset generation. Add Promptfoo if security and adversarial robustness are important.

One question that would help me narrow this down: Are you testing a general-purpose base model, a fine-tuned model, or an LLM application such as a chatbot or RAG system? That distinction can change which generator is best.

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