Lightning Rod - Turn real-world data into training datasets fast
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Lightning Rod SDK turns real-world data — like news, filings, or your own documents — into verified, production-ready training datasets in hours using just a few lines of Python. Skip manual labeling and synthetic guesswork.
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Lightning Rod: Generate training data
Hi Product Hunt! Ben here, founder of Lightning Rod.
We started Lightning Rod because training data is the blocker for most AI projects. Companies have a huge amount of valuable historical data and access to rich public sources, but turning it into something AI can actually learn from is too slow and expensive.
Today we’re launching our training data SDK, which lets you automatically generate LLM-ready training data from raw documents or public sources. We use real-world sources and outcomes over time as supervision — no labeling or annotation required ⚡
Here’s what you get:
Go from idea to dataset, fast. Define your criteria and data source. We collect and label training data for you — ready in minutes, from just a few queries or examples.
Use your own data or start from public data sources. Generate training data from internal documents like emails, tickets, and logs, or from integrated public data sources.
Provenance in every row. Every record links back to its source, so you can audit what went into your model.
Quality built in. Automated scoring and filtering remove low-confidence examples and outputs that do not follow your instructions.
Turn historical data into training signal. We use real-world outcomes over time to convert your timestamped docs, tickets, logs, and news into grounded supervision automatically.
We’ve already used data generated with this platform to beat frontier models 100x larger, and to train domain expert models on everything from corporate risk to sports predictions.
Create your first dataset free at lightningrod.ai. Use code ProductHunt50 for $50 in free credits.
Thanks for checking us out — I’ll be here all day reading and replying. If there’s a dataset or model you’ve wanted to build, drop it in the comments and we’ll help you get started!
@bturtel the logo looks like the one of the Wallet of Satochi - please consider changing it! ? This might be copyright violation!
@bturtel Congrats on the launch Benjamin and team! Good hunt, @fmerian :)
As a marketer, I’m thinking about using this for content datasets. Any examples you have seen in my niche?
Lightning Rod: Generate training data
@rohanrecommends We don't have any public examples to share, but we are working with a customer to predict which messaging and content will drive the highest performance.
Content datasets are a great fit for what we do. Would love to chat and learn more about your use case!
Lightning Rod: Generate training data
Thanks @rohanrecommends - yes, content marketing is a very natural fit for us.
One strong use case is generating training data to predict which messages, hooks, claims, or creative variants are most likely to perform with a given audience. We’re currently working on a case study around predicting outcomes of content experiments.
Over time, that can mean generating large sets of message ideas, ranking the ones most likely to land, and helping teams iterate faster on what works.
We don’t have a public example yet, but we’re hoping to share results within the next month.
@bturtel Congratulations on the launch! What's one underrated data source (like support tickets or emails) you've seen unlock massive gains in custom LLM training for non-tech founders?
Lightning Rod: Generate training data
@swati_paliwal Thanks! Any timestamped internal docs where you already know what happened next — quarterly reports, risk assessments, customer communications. Stuff companies have years of and never think of as training data.
The other really powerful source for domain-expert models is news. In most domains, forcing a model to learn to predict outcomes from news forces the model to really learn everything about that domain. So its a really fast and scalable way of training domain-expert AIs on the fly.
Any benchmarks you can share?
Lightning Rod: Generate training data
@zerotox Thank you for your support! We've published some of our research and benchmarks here: https://www.lightningrod.ai/about
Here's a couple of highlights, but let me know if there is something specific you'd like to know more about:
We've ranked #1 and outperformed GPT-5.2 and Gemini 3 Pro on Prophet Arena Sports, a leaderboard from the University Of Chicago.
We outperformed Gemini 3 Pro, Claude Sonnet 4.5, and o3 on a benchmark by Forecasting Research Institute.
We've published research showing how our Future As Label approach can outperform frontier models on accuracy and calibration.
Lightning Rod: Generate training data
@zerotox Yes - we have a page with a handful of our wins and published research here: https://www.lightningrod.ai/about
Lightning Rod: Generate training data
@zerotox Hi Kumar, I will add that we did a test on an earlier model we trained with this data generation technique where we made live predictions for questions on polymarket with our model and a handful of much larger frontier models, wait about a month for most of the questions to resolve, and then see who did better - results here: https://blog.lightningrod.ai/p/foresight-32b-beats-frontier-llms-on-live-polymarket-predictions
Congrats!! Any plans to a no-code interface for non-technical teams?
Lightning Rod: Generate training data
@himani_sah1 Thank you for the support! In addition to the SDK, we also have an AI agent that helps you make datasets. I'm not technical and I use it to make datasets all the time. It's available here: lightningrod.ai. Give it a try and let us know what you think! It's super easy to use.
Lightning Rod: Generate training data
@himani_sah1 Hi Himani, we do have a no-code interface in our dashboard: dashboard.lightningrod.ai - you can either chat with an agent to set something up or manually configure a data generation pipeline in the UI. And we will definitely be expanding on that in the near future!
Lightning Rod: Generate training data
@himani_sah1 Yes! We just launched our "Prompt to fine-tune" agent as well to help non-technical users build datasets and fine-tune models without any code. I'd love to hear what you think!
ConnectMachine
How does the quality scoring work... Is it model-based or rule-based filtering?
Lightning Rod: Generate training data
@syed_shayanur_rahman We support a combination of both. Here is an example of LLM model based scoring: https://docs.lightningrod.ai/python-sdk/dataset-generation/labeling-and-context#filtercriteria
Using real-world outcomes over time as automatic supervision instead of requiring manual labeling is a fundamentally different approach to training data generation — it means the dataset quality improves with historical depth rather than human annotation effort, which should scale much better for domain-specific fine-tuning. The claim of beating frontier models 100x larger with data generated through this platform is compelling; for teams working with internal documents like support tickets or emails, how does Lightning Rod handle PII in the source material — is there automated redaction before training data generation, or does that fall on the user?
Lightning Rod: Generate training data
@svyat_dvoretski That is a good point! Lightning Rod SDK fits easily into any kind of data processing pipeline so if you did want to redact PII before creating seeds you definitely could. In the Lightning Rod SDK though you can include instructions for how to turn the seed data into questions, and examples. That could include instructions and examples for how to mutate any PII or just what type of questions you want to generate from your data. Of course any data uploaded is secure and scoped to your organization. Let me know if you want to me to walk you through sometime how to configure that!
Lightning Rod: Generate training data
@svyat_dvoretski Appreciate the support! In many of the domains we work in, data security and governance are a core requirement. Our system is exposed through APIs and can be deployed directly within your own cloud or environment. So there’s no requirement to move sensitive data outside your infrastructure.
Banyan AI Lite
Congrats team! Question: How do you ensure the generated datasets are actually suitable for fine tuning, given the noise, bias, and duplication often present in public news sources? Do you apply any validation, deduplication, or labeling quality checks, and can users control how the data is structured or filtered for specific domains or tasks?
Lightning Rod: Generate training data
@davitausberlin good question!
We know the training data is high-quality because of the results we've achieved across a variety of benchmarks and domains. We often beat frontier LLMs much larger (10-100x) by using this to fine-tune small models. Not just evals we designed on our own questions, but often in independent leaderboards. You can see a few wins / proof points here: https://www.lightningrod.ai/about
On validation: Yes, we have a bunch of quality checks built in, and by default low-confidence answers get dropped automatically. All steps are configurable, and you can also attach LLM-scored filters at the seed and question level with your own rubrics to filter by: https://docs.lightningrod.ai/python-sdk/dataset-generation/labeling-and-context
Before training we also run deduplication and other configurable data preparation steps: https://docs.lightningrod.ai/python-sdk/fine-tuning-beta/data-preparation
I'd love to hear your feedback if you give it a shot.
Lightning Rod: Generate training data
@davitausberlin Great question - We do have a configurable deduplication step in our pipeline before fine-tuning. On our larger training runs we have also generated samples from the GDELT project which is an aggregate database of "events" which are in a sense de-duplicated news articles, and we will select the top events over time to generate forward-looking training samples from. Our pipeline offers a seed generator that uses this same system, which is good for building or evaluating over general forecasting questions. If you are fine-tuning on a specific domain you can also generate seeds from specific news queries or sources.
We're doing some ML work on our side for matching and recommendations so this is relevant. Can the SDK work with proprietary data like internal user behavior logs, or is it mainly designed around public sources for now?
Lightning Rod: Generate training data
@ben_gend Yes, a lot of our customers are using proprietary data! You can do this with our filesets feature: https://docs.lightningrod.ai/python-sdk/dataset-generation/filesets
Let us know if you have any questions, happy to help get you up and running on the platform1
Lightning Rod: Generate training data
@ben_gend 100% - we (unsurprisingly) see the strongest improvements over frontier models when training on proprietary internal data.
If you want to try the SDK, we have some example notebooks for this here https://github.com/lightning-rod-labs/lightningrod-python-sdk/tree/main/notebooks/custom_filesets
Also happy to meet and hear about your use case if we can help you get started!
Lightning Rod: Generate training data
@ben_gend Hi Ben, we definitely support bringing your own data to transform it into training samples or augment it with additional context or labels. There are different ways to approach this. We have an example here for how to create a dataset from your own data (pdfs, csvs, etc) that can be processed further with our pipeline here.
We also support as Gretchen mentioned creating custom "Filesets" which can be used to process those documents by chunking them, or by indexing in a RAG database and generating specific types of questions that way. This is how we trained our SEC model for example.
If you do want to do an experiment with custom data I'd definitely encourage finding time to chat more about your use case.
Generate training data? What does it mean? Congrats on the launch, @bturtel!
Lightning Rod: Generate training data
@neilverma Thank you for the support! If you want to fine tune a model you need data, and the quality of that data matters a lot for your final results. Our SDK is designed primarily to generate high-quality training data either from your own documents or just from news or other public data sources, to train models that make more accurate and well calibrated predictions. We have shown this can apply to a wide variety of domains. But it is a flexible system that can also be used for things like evaluation, classification, SFT, even lead generation. I think of it like a cookbook for taking in any kind of raw data, and turning it into the format you need, quickly and at scale. Let us know if you want to chat through how this can be applied to your use case!
Lightning Rod: Generate training data
Thanks@neilverma !
We turn raw enterprise documents and public sources into verified training dataset, so companies can fine-tune useful models without hand-labeling. We basically use real-world outcomes as supervision instead of asking teams to label everything by hand.
Lightning Rod: Generate training data
@neilverma Thank you for supporting our launch, it means a lot 💛
Congrats on the launch!
Very relevant problem - everyone talks about models, but high-quality training data is still the real bottleneck.
Love the emphasis on provenance and production-ready datasets. Strong positioning. Wishing you a great launch today 🙌
Lightning Rod: Generate training data
@mikita_aliaksandrovich Thank you for the support, we really appreciate it 💛
@gretchen_y You are welcome!
Trufflow
What ways could I validate that the training data is actually improving downstream model performance?
Lightning Rod: Generate training data
@lienchueh Good question!
The SDK has a built-in evaluation module so you can measure improvement over your base model directly on held-out test sets: https://docs.lightningrod.ai/python-sdk/fine-tuning-beta/evaluation
You can also run rollouts against frontier LLMs on the same questions and score everything against ground truth (Brier score, calibration error, etc.): https://docs.lightningrod.ai/python-sdk/dataset-generation/rollouts-and-scoring
Examples of how we've done this in our notebooks (https://docs.lightningrod.ai/python-sdk/getting-started/examples) and research papers (https://www.lightningrod.ai/about).