Dang-DN-VN/saas-product-support-sharegpt-1k

SaaS/Tech Product Support — Multi-Turn SFT Dataset

A domain-specific supervised fine-tuning dataset for SaaS and tech product support conversations, built for LLM fine-tuning and instruction tuning.

Dataset Summary

This dataset contains 1,200 multi-turn English conversations between a customer and a support agent, covering common SaaS/tech support scenarios: bug reports, billing issues, API errors, authentication problems, onboarding blockers, integration failures, and feature requests.

All samples follow the ShareGPT format with natural multi-turn dialogue (2–5 complete exchanges per conversation).

Format

Each sample follows the ShareGPT format:

{
  "conversations": [
    {"from": "human", "content": "User message describing a technical issue"},
    {"from": "gpt", "content": "Agent response with clarification or troubleshooting step"},
    {"from": "human", "content": "User follow-up with additional details"},
    {"from": "gpt", "content": "Agent resolution or clear next step"}
  ]
}

Quality Pipeline

This dataset was produced by a 5-stage pipeline:

1. 1.Coverage-aware generation - a diversity planner maps the domain into sub-topics and intents before generation, ensuring broad scenario coverage across the full dataset
2. 2.Schema validation and token length filtering - malformed or out-of-range samples are rejected before scoring
3. 3.Dual deduplication - MinHash (lexical) followed by semantic deduplication via embedding similarity (0 duplicates detected across 1,200 samples)
4. 4.LLM-as-judge scoring - samples are scored on relevance, factual consistency, format quality, and response usefulness
5. 5.Human review - each sample was manually evaluated and either approved, edited, or rejected

Only samples that passed all five stages are included.

Generation Stats

Diversity Coverage

Sub-topics covered include (but are not limited to):

• —Billing & subscription management
• —Authentication and account access
• —API errors and integration failures
• —Performance and reliability issues
• —Data inconsistencies and reporting bugs
• —Onboarding and product setup
• —Feature requests and product feedback
• —Security and privacy concerns

Intended Use

• —Supervised fine-tuning of base models (LLaMA, Mistral, Qwen, Phi, etc.) for customer support conversation tasks
• —Domain adaptation for SaaS support chatbots
• —Instruction-tuning layer for multi-turn assistant workflows
• —Compatible with Axolotl, LLaMA-Factory, Unsloth, and hosted fine-tuning APIs

Limitations

• —Synthetic data generated by an LLM and verified by human review - not a substitute for real customer interaction logs
• —Domain-specific dataset; not designed for broad general-purpose instruction following outside the SaaS/tech support domain
• —Real-world edge cases and highly product-specific terminology may require supplementary domain-specific examples

Need a Larger or Custom Dataset?

This dataset was built using the same pipeline I offer as a freelance service. If you need a larger support dataset, a different vertical (e-commerce, fintech, healthcare SaaS, etc.), or a completely different domain, you can order a custom dataset here:

👉 SFT Dataset Preparation — Fiverr

Custom domains, sample counts, and formats (Alpaca or ShareGPT) are available on request.

Author

Dang Phan (Dangineer) AI Engineer · LLM Fine-Tuning Specialist thanhdangphan1510@gmail.com