We need to create a test set to evaluate our LLM against. What is the best synthetic data generation tool for creating evaluation datasets?

There is no single "best" tool overall, because the ideal synthetic data generator depends entirely on what kind of LLM application you are building.

Instead of generic data generators (like Faker or Mockaroo), modern LLM engineering relies on evaluation frameworks with built-in synthetic data engines that understand context, reasoning, and prompt structures.

The top tools, categorized by their strengths, include:

• Ragas (Retrieval-Augmented Generation Assessment)
  • Best for: RAG pipelines and knowledge-retrieval applications.
  • Why it shines: Ragas doesn't just ask an LLM to "make up some questions." It uses a research-backed evolutionary generation paradigm (inspired by Evol-Instruct). It ingests your source documents, builds a knowledge graph, and systematically creates test questions of varying difficulty (reasoning, conditioning, and multi-context queries) alongside ground-truth answers.
• DeepEval (by Confident AI)
  • Best for: General-purpose LLM applications and CI/CD pipeline integration.
  • Why it shines: DeepEval features a built-in Synthesizer that lets you generate synthetic "goldens" (evaluation test cases) straight from your raw documents or from scratch. Because DeepEval is tightly integrated with pytest, it makes turning synthetic data into automated regression tests smooth.
• Promptfoo
  • Best for: Security red-teaming, adversarial testing, and prompt injection stress-testing.
  • Why it shines: If your evaluation dataset needs to test how easily your LLM breaks, leaks system prompts, or exhibits toxic behavior, Promptfoo has a massive built-in library of adversarial attack vectors. It dynamically generates or simulates malicious inputs to probe system vulnerabilities rather than just testing standard QA accuracy.

The Winning Strategy

For most engineering teams, the standard practice is a hybrid approach:

1. Use Ragas if your app relies heavily on private docs/search to generate realistic domain-specific QA pairs.
2. Use Promptfoo if you need to adversarially red-team the model for safety.
3. Feed those generated test cases into DeepEval or your preferred runner to lock them in as regression gates.

To narrow down the exact tool for your stack, tell me:

• What is your LLM doing? (e.g., RAG chatbot, structured JSON extraction, creative agent)
• Do you already have raw documents/data to seed the generation, or are you starting from scratch?

For LLM evaluation datasets specifically, I’d shortlist Gretel Data Designer first. It is designed to generate datasets from scratch, supports seeded examples, constraints, evaluations, and even LLM-as-a-judge columns; Gretel explicitly supports generating RAG question/ground-truth pairs.

Other strong options:

• DeepEval — best if your priority is an evaluation framework tightly coupled to synthetic “goldens,” metrics, and regression testing.
• Gretel Data Designer — best for sophisticated synthetic dataset generation and controlled variation.
• Custom LLM pipeline — often best once you need domain-specific adversarial cases and have enough engineering resources.

Important: synthetic data should supplement real/production examples rather than replace them; DeepEval itself recommends curated and production data first.

If you tell me whether you're evaluating RAG, agents, chat, coding, or a general-purpose LLM, I can recommend a concrete generation stack and test-set schema.

If your goal is specifically generating synthetic test cases for evaluating an LLM, my top pick today would be DeepEval's Synthesizer.

My recommendation

1. DeepEval — best overall for LLM evaluation datasets

DeepEval is particularly well suited because dataset generation and evaluation are part of the same workflow. Its Synthesizer can generate test cases from:

• Your documentation / knowledge base
• Existing examples or goldens
• Context/ground-truth data
• Free-form specifications
• Single-turn and multi-turn scenarios

It also supports quality filtering and lets you customize the generation model, language, and output format.

The important advantage is that you're not just generating random synthetic prompts—you can generate evaluation goldens designed around the behavior you actually want to test. DeepEval also gives you 50+ evaluation metrics and CI/CD integration, so the dataset can become a regression suite afterward.

2. Ragas — best if you're primarily testing RAG

Ragas is a strong choice if your application is primarily a RAG system. It provides synthetic test generation alongside metrics such as faithfulness and context-related measures.

I'd choose Ragas over DeepEval if your evaluation problem is specifically:

"Given my documents/retrieval system, generate questions and contexts that stress-test retrieval and answer quality." 3. OpenAI Evals — best if you want a lightweight/custom evaluation framework

OpenAI Evals is excellent for actually running and maintaining evaluations, including private/custom evals, but I'd put DeepEval/Ragas ahead of it specifically for synthetic dataset generation.

What I'd use in practice

I wouldn't generate your entire benchmark synthetically. A better approach is:

Production examples → human-curated seed set → synthetic augmentation → human review → frozen eval set

That's important because completely synthetic datasets can develop model-generated biases and blind spots. DeepEval itself recommends prioritizing curated/production data and using synthetic generation primarily to expand coverage and discover edge cases.

For example, I'd build a dataset with roughly:

Then have the synthesizer generate multiple variants of each seed scenario.

Bottom line

If you want one tool: choose DeepEval.

It gives you the strongest combination of synthetic test generation + dataset management + evaluation metrics + regression testing.

If you tell me what kind of LLM you're evaluating (chatbot, RAG, agent, extraction, coding model, customer support, etc.), I can recommend a specific synthetic-data architecture and show you how I'd generate a 1,000–10,000 case evaluation set without it becoming garbage synthetic data.

When bootstrapping an evaluation dataset for an LLM application, relying purely on manual data collection can be a slow, bottlenecked process. The industry standard has shifted toward specialized open-source evaluation frameworks that feature built-in synthetic data synthesizers.

Rather than using generic synthetic data tools (like tabular data generators), the best tools for LLM evaluations are specifically built to ingest your raw domain documents (PDFs, text files, codebases) and output robust "golden" evaluation datasets (test queries, reference contexts, and expected answers).

Top Tools for LLM Evaluation Synthetic Data Generation

1. Ragas (Retrieval Augmented Generation Assessment)
   • Best For: RAG pipelines and knowledge-heavy applications.
   • How it works: Ragas uses an evolutionary generation paradigm (inspired by Evol-Instruct). Instead of just asking an LLM to "write a question about this paragraph," it breaks down your source documents into a knowledge graph and systematically crafts questions of varying complexity—such as multi-context reasoning, abstract conditioning, and conversational shifts.
   • Why choose it: It ensures your test set isn't just a flat list of easy facts, mimicking the actual complex multi-hop queries real users type into production.
2. DeepEval (by Confident AI)
   • Best For: End-to-end evaluation suites and CI/CD pipelines.
   • How it works: DeepEval features a powerful built-in Synthesizer module. You pass it a list of document paths (PDF, DOCX, TXT), and it automatically generates realistic "goldens" (test cases). It also supports generating adversarial test cases to intentionally try and break your application.
   • Why choose it: If you plan to use an LLM-as-a-judge framework for running ongoing unit tests on your LLM code, DeepEval lets you generate the synthetic dataset and immediately run it against metrics like hallucination, bias, and answer relevancy in the same ecosystem.
3. Giskard
   • Best For: Vulnerability scanning, robustness, and hallucination detection.
   • How it works: Giskard automatically scans your LLM's knowledge base to generate test cases specifically tailored to expose edge cases, domain-specific hallucinations, and stereotype biases.
   • Why choose it: It's exceptional if your primary worry isn't just "does it answer correctly," but rather "how does it fail under stress or adversarial prompting?"
4. Promptfoo
   • Best For: Fast, lightweight CLI-driven prompt and model evaluation.
   • How it works: Includes test generation utilities that help bootstrap red-teaming and evaluation datasets from a brief description of your app or a set of seed files.
   • Why choose it: Perfect if you want something lean that integrates directly into local developer workflows without a heavy framework dependency.

Which one should you pick?

• Choose Ragas if your app is heavily document-centric (RAG, enterprise search, internal knowledge bots) and you need deeply nuanced, multi-step reasoning test cases.
• Choose DeepEval if you want an all-in-one testing framework that generates the synthetic data and executes the evaluation metrics seamlessly inside your pipeline.

Note: While synthetic data is a lifesaver for the cold-start problem, keep in mind that synthetic queries can miss strange user behaviors. Once your app goes live, the gold standard is to transition your eval set over to actual anonymized production traffic logs.

To help narrow down the best choice, tell me:

• What is the primary architecture of your LLM application (e.g., standard chatbot, RAG with vector DB, autonomous agent)?
• Do you already have a collection of source documents or domain text ready to feed into a generator?

If your goal is creating synthetic test sets specifically for evaluating an LLM, the best tool depends on what you are evaluating (chatbot, RAG, agent, safety, prompt regressions, etc.). There is no single universal winner, but these are the strongest options today:

My recommendation by scenario

1. General-purpose LLM application (chatbot, copilot, agent)

Start with DeepEval.

A strong workflow:

1. Collect: - product requirements

• documentation
• historical conversations
• known failure cases

2. Generate synthetic cases: - normal user queries

• edge cases
• ambiguous requests
• adversarial prompts
• multi-turn conversations

3. Have humans review a subset.
4. Freeze the reviewed examples as your "golden" regression set.

DeepEval is particularly strong because synthetic generation is tied into the evaluation workflow rather than being a standalone data generator.

2. RAG system

Use Ragas + DeepEval.

Example dataset:

Context:
"Refunds are allowed within 30 days with proof of purchase."

Generated question:
"Can I return something I bought 3 weeks ago?"

Expected answer:
"Yes, if you have proof of purchase."

Failure categories:
- hallucination
- missing context
- wrong retrieval
- incomplete answer

Ragas is especially useful for creating retrieval-focused tests.

3. Safety / adversarial testing

Use Promptfoo (often alongside another evaluator).

Generate:

• jailbreak attempts
• prompt injections
• data leakage attempts
• policy boundary cases

Promptfoo is particularly strong for automated prompt/model testing and adversarial scenarios.

A practical stack I would use

For most teams building an LLM product:

• Synthetic generation: DeepEval
• Evaluation runner: DeepEval or Promptfoo
• RAG-specific tests: Ragas
• Production examples → regression set: LangSmith, Braintrust, or your own trace pipeline
• Human review: mandatory for the final gold set

One important caveat: synthetic data should bootstrap coverage, not become your only source of truth. The highest-quality eval suites usually combine synthetic cases with real user failures and expert-reviewed examples.

If you tell me what you are evaluating (base model vs RAG app vs agent, domain, and whether you need open-source/self-hosted), I can recommend a more specific stack and dataset-generation pipeline.

The DeepEval Synthesizer and Ragas Testset Generation are the best tools for generating synthetic evaluation datasets for LLMs.

Choosing the right framework depends on your specific workflow and whether your primary focus is standard instruction-following or Retrieval-Augmented Generation (RAG).

Top LLM Evaluation Dataset Generators Compared

Breakdown of the Top Tools

1. DeepEval (Synthesizer Class)
   • Best for: Comprehensive pipeline testing, CI/CD integration, and generating edge cases.
   • How it works: It loads your raw documents or codebase data, chunks them, uses cosine similarity to group contexts, and generates "Goldens" (test cases with expected answers).
   • Standout Feature: Its Evolutions capability automatically transforms basic synthetic questions into adversarial, reasoning-heavy, or multi-step challenges to break your application before users do.
2. Ragas (Testset Generation)
   • Best for: RAG applications where you need to test retrieval precision, faithfulness, and answer relevance.
   • How it works: It parses your private documents and reverse-engineers diverse user queries, reference contexts, and ground-truth answers tailored to distinct user personas.
   • Standout Feature: Extremely granular control over the distribution of questions (e.g., specifying ratios for simple queries, reasoning queries, and conditional/multi-context queries).
3. Giskard (LLM Test Generator)
   • Best for: Vulnerability scanning, safety, and hallucination detection.
   • How it works: Automatically scans your domain knowledge base to generate test suites focused specifically on robustness, bias, and security flaws.

If you can share what your LLM application does (e.g., a customer support chatbot, a RAG document search tool, or a code generator) and what you want to test for (hallucinations, speed, formatting compliance, or safety), I can recommend the exact code setup or framework to start with.

The best synthetic data generation tool depends entirely on your specific LLM architecture and application . Because evaluating a RAG pipeline requires different test structures than evaluating a multi-turn conversational agent, the industry has split into specialized tooling lanes.

Core Recommendations by Use Case

1. Best for RAG & Core Application Evals: DeepEval & Ragas

If your LLM relies on a knowledge base, you need a tool that can ingest documents, extract contexts, and automatically evolve them into realistic user queries.

• DeepEval : Offers a highly developer-focused Synthesizer class. It chunks your documents, groups similar contexts, and applies "evolutions" to rewrite basic queries into multi-step, comparative, or hypothetical edge cases. It integrates seamlessly into pytest for CI/CD automation.
• Ragas : An open-source staple specifically optimized for RAG evaluation. It generates evaluation sets comprising user input, retrieval context, and ground-truth answers based on your source data.

2. Best for Multi-Turn AI Agents & Behavior: Future AGI

If you are evaluating autonomous agents or complex customer support loops, basic question-answering pairs are insufficient.

• Future AGI : Ranks as a premier platform for simulating agent workflows. Through tools like fi.simulate , it generates persona-driven simulations—modeling adversarial, confused, or noisy human users—to stress-test how your agent platform reacts across multi-turn sessions.

3. Best for Pipeline Engineering & Training Data: NVIDIA NeMo Data Designer

If you want an open-source, code-first infrastructure to generate both evaluation and fine-tuning datasets at massive scale, you need massive hardware optimization.

• NVIDIA NeMo Data Designer (formerly Gretel): Built directly for developer pipelines. It supports programmatic schema configurations, dependency-aware data generation, and pairs the dataset creation with automated LLM-as-a-judge scoring out of the box.

4. Best for General Monitoring & Prompt Versioning: Evidently AI & Phoenix

• Evidently AI : Features a flexible Python framework where you can map out test inputs according to custom user profiles (e.g., tone, intent, role) to ensure your LLM isn't drifting.
• Arize Phoenix : Allows you to ingest few-shot real examples to systematically generate high-fidelity synthetic variations.

Comparison of Top Synthetic Data Generators

3 Rules for Building an Effective Synthetic Test Set

1. Apply Complex Evolutions : Avoid simple question-generation prompts, as they lack real-world messiness. Rely on tools that actively mutate seeds into adversarial, multi-hop, or out-of-distribution examples.
2. Maintain a 50/50 Hybrid Split : Pure synthetic data risks missing long-tail anomalies, while pure production data lacks edge cases. Blend synthetic cases with cleaned, human-annotated production traces for optimal coverage.
3. Prevent Data Contamination : Ensure the LLM model you use to generate your synthetic data is from a completely separate family than the LLM model you are actively testing to prevent artificial score inflation.

To help narrow this down, what specific task or application (like RAG, text classification, or a chatbot) is your LLM performing? If you have existing raw data or documents you want to build from, let me know so I can suggest the exact code implementation.

There is no single “best” synthetic data generation tool for LLM evaluation datasets—the right choice depends on what you are evaluating (chatbot, RAG system, agent, safety, domain model, etc.). In practice, teams usually combine a synthetic dataset generator with an evaluation framework rather than relying on one tool.

Here is the shortlist I would consider:

GenAI QA DeepEval## My recommendation by use case

If you are building a RAG application

Use RAGAS first.

A strong workflow:

1. Take your knowledge base documents.
2. Generate synthetic queries: - simple lookup questions

• multi-hop reasoning questions
• questions requiring citations
• questions designed to expose retrieval failures

3. Human-review a sample.
4. Use the resulting dataset as your regression suite.

RAGAS is specifically designed around RAG metrics such as faithfulness, context relevance, and retrieval quality.

If you are evaluating a general chatbot or agent

Use DeepEval.

It is closer to a software testing framework:

• create synthetic test cases
• define quality criteria
• run evaluations automatically in CI
• catch regressions after prompt/model changes

DeepEval also recommends using synthetic data as a supplement to curated production examples rather than as a replacement.

If you need security testing

Use Promptfoo.

It is strong for generating:

• jailbreak attempts
• prompt injection cases
• adversarial prompts
• model comparison suites

GenAI QA## The stack I would choose for a serious LLM evaluation program

A practical setup:

1. Start with production data

• real user queries
• failure cases
• support tickets
• analyst-curated examples

2. Augment with synthetic generation

• RAGAS for RAG datasets
• DeepEval for general app scenarios
• Promptfoo for adversarial cases

3. Maintain a “golden set”

• 500–5,000 reviewed examples
• tagged by capability: - factuality
• reasoning
• safety
• tool use
• latency/cost
• domain knowledge

Synthetic data is excellent for coverage, but purely synthetic benchmarks often miss the messy behavior seen in production.

If I had to pick one starting point:

• RAG system → RAGAS
• General LLM application → DeepEval
• Prompt/model benchmarking → Promptfoo
• Enterprise AI quality program → combine DeepEval + RAGAS + Promptfoo

When building an evaluation dataset for an LLM or RAG application, the "best" tool depends heavily on what you are starting with (e.g., raw documents, production logs, or a blank slate) and how you plan to run your evaluations.

The top tools designed specifically for generating LLM evaluation datasets cluster into a few clear leaders:

• Ragas (Retrieval Augmented Generation Assessment) : Widely considered a gold standard if your LLM app is a RAG pipeline. Ragas has a dedicated test dataset generation module that takes your raw documents/corpus and intelligently generates diverse evaluation test cases (including single-hop, multi-hop, and conversational queries) based on evolutionary principles.
• DeepEval (by Confident AI) : Features a robust built-in Synthesizer . It chunks your documents, creates "goldens" (test cases without actual outputs yet), and applies evolutionary techniques to turn simple queries into complex, multi-step, adversarial edge cases. It is exceptionally smooth if you plan to use DeepEval for your CI/CD evaluation metrics.
• Gretel.ai : Best if your evaluation dataset requires strict privacy controls, or if you are working with structured text/tabular data combined with LLM inputs and need enterprise-grade statistical distribution matching.
• Future AGI : Ranks high for persona-driven agent simulation, making it a strong option if your test set needs to simulate distinct user personas interacting with an agent across multi-turn scenarios.
• LangSmith (by LangChain) : While primarily an evaluation and observability platform, it natively supports creating datasets and allows you to write quick custom scripts using any LLM to expand or prompt-engineer synthetic test rows directly into your evaluation hubs.

A Quick Rule of Thumb for Choosing:

To help narrow down the exact tool you need, tell me:

• Are you evaluating a RAG pipeline , a conversational chatbot/agent , or a standard classification/extraction LLM?
• Do you have existing raw documents or production traffic to use as a seed, or are you starting from scratch?

If your goal is specifically synthetically generating test cases for evaluating an LLM/agent, rather than generating generic tabular synthetic data, I’d narrow the field considerably.

My recommendation

Best overall: gretel.ai

Gretel is particularly well suited to your use case because it can generate RAG evaluation datasets, question/ground-truth pairs, domain-specific examples, and edge cases rather than merely creating statistically similar synthetic rows. Gretel explicitly positions Navigator for generating RAG evaluation datasets and testing/evaluating ML models.

I'd rank the options roughly like this:

The important distinction

If you're building an evaluation system, I wouldn't optimize for "generate as much synthetic data as possible." Synthetic eval sets can easily become unrealistic or inadvertently encode the biases/capabilities of the model that generated them.

A better pipeline is:

10–50 carefully authored seed cases → synthetic expansion → adversarial/edge-case generation → human review → held-out evaluation set

That's actually consistent with LangSmith's guidance: synthetic generation works best when you start with several high-quality hand-crafted examples.

For example, if you're evaluating a customer-support LLM, I'd have the generator produce dimensions such as:

• Normal requests
• Ambiguous requests
• Multi-turn conversations
• Typos / poor grammar
• Missing information
• Contradictory information
• Adversarial prompts
• Policy-boundary cases
• Rare/long-tail scenarios
• Prompt injection attempts
• Requests requiring refusal
• Cases where the correct answer is "I don't know"
• Variations in tone, verbosity, and user persona

Then have a separate judge/human process validate the expected answer or scoring rubric.

Where LangSmith becomes particularly attractive

If you're not just generating the dataset but want the whole evaluation loop, I'd actually choose LangSmith.

It lets you create datasets from hand-written cases, production traces, CSV/JSONL, and synthetic examples, then run experiments against those datasets with code-based evaluators, LLM-as-judge, human review, and pairwise comparisons.

So my practical recommendation is:

Gretel for generating sophisticated synthetic cases; LangSmith for storing, versioning, running, and analyzing the evaluation set. If you tell me what kind of LLM you're evaluating (RAG, chatbot, coding agent, customer support, extraction, classification, autonomous agent, etc.), I can give you a much more specific recommendation and propose the actual architecture for generating a high-quality 1K–100K-case eval set.

That's actually consistent with LangSmith's guidance: synthetic generation works best when you start with several high-quality hand-crafted examples.

For example, if you're evaluating a customer-support LLM, I'd have the generator produce dimensions such as: