# Synthetic dataset generation inside AgentClash

Generate eval-ready examples from pinned seeds without leaving your workspace. Choose fast prompt-only expansion or Agentic Self-Instruct with weak-vs-strong judge filtering.

Source: https://www.agentclash.dev/features/synthetic-dataset-generation
Markdown export: https://www.agentclash.dev/md/features/synthetic-dataset-generation

## Generation strategies

Fast Self-Instruct adds volume quickly. Agentic Self-Instruct runs weak and strong solver rollouts with acceptance policies tuned to the useful difficulty zone.

- OpenTelemetry-compatible trace import
- Pinned datasets and golden test cases
- Baseline versus candidate regression checks
- Replay trails for tool calls, outputs, and artifacts
- Scorecards for correctness, cost, latency, and evidence
- CI gates for prompt, model, RAG, and tool changes

## From generation to gates

### 1. Import the evidence

Start from OpenTelemetry traces, curated datasets, support transcripts, or a real failure your team already saw.

### 2. Pin the baseline

Record the current accepted behavior so every prompt, model, RAG, or tool change has a fair comparison point.

### 3. Replay the evidence

Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence when a candidate gets worse.

### 4. Gate the release

Compare candidate and baseline runs, then fail CI before a regression reaches users.

## Run your first job

Open a dataset, start synthetic generation, then baseline and gate the accepted rows.

- [Agent evals](https://www.agentclash.dev/agent-evals): Real-task agent evals with replay evidence and CI gates.
- [LLM agent evaluation](https://www.agentclash.dev/llm-agent-evaluation): Evaluate LLM agents on full trajectories, not one-shot answers.
- [Compare tools](https://www.agentclash.dev/compare): See how AgentClash differs from prompt-eval platforms.
- [Synthetic generation guide](https://www.agentclash.dev/docs/guides/synthetic-dataset-generation): Docs for UI and CLI generation jobs.
- [DataSmith platform page](https://www.agentclash.dev/platform/datasmith): Offline SDK for training export.
- [Datasets overview](https://www.agentclash.dev/docs/guides/datasets-overview): Import examples, record baselines, sync regression suites, and gate CI.
- [Dataset CI gates](https://www.agentclash.dev/docs/guides/dataset-ci-gates): Fail builds when a candidate regresses against a pinned baseline.
- [CI/CD agent gates](https://www.agentclash.dev/docs/guides/ci-cd-agent-gates): Block pull requests when agent behavior gets worse.

## Synthetic generation FAQ

### Where do I start generation in AgentClash?

Open Workspaces, Datasets, your dataset, then Synthetic generation in the UI or use agentclash dataset generate from the CLI.

### What happens to rejected examples?

Rejected rows are stored with reason codes and solver attempts so you can review why the judge declined them.

### Can I export for fine-tuning from AgentClash?

AgentClash optimizes for eval formats. For SFT, DPO, and Hugging Face export, use the DataSmith Python SDK on the same seeds.