TLDR:
LLM training data: Like a library frozen in time—it has all books up to a certain year, but nothing published after that.
Even if Hatz shows a later date, it’s still the same underlying model with the same knowledge limitations.
Hatz AI might show a later training date when prompting the LLM because it doesn’t store or track the model’s actual "knowledge cutoff date." This difference doesn’t mean the model is more up-to-date—it’s simply a variation in how the date is displayed. Platforms like OpenAI explicitly include the true knowledge cutoff date in their system, while Hatz does not.
Live AI Powered search tools from the Tool Selector (e.g., Firecrawl): Like an open internet connection—it finds the latest news, even from articles published 5 minutes ago.
Why is it that Hatz sometimes displays a different training date?
Hatz AI may sometimes display a training date that appears later than the official knowledge cutoff date of the underlying large language model (LLM). This is because Hatz does not store or manage the actual training cutoff date of the model. Instead, it may display the current system date or a placeholder date, which can create the impression that the model has been trained on more recent data. However, it’s important to note that the model itself is not more up-to-date. The underlying LLM, operates with the same knowledge cutoff provided by its original training set by the model provider. This discrepancy is simply a difference in how dates are handled and displayed, and Hatz uses the same core model as other platforms.
Understanding LLM Libraries vs AI Powered Search Tools
1. What are LLM training dates?
When people talk about a training date for a large language model (LLM), they’re referring to the most recent time period that the model's training data includes. For example, if the training data of a model like GPT-4 has a "knowledge cutoff date" of October 2023, that means:
The model was trained on a dataset containing information up until October 2023.
The model doesn’t "know" anything that happened after that date unless it’s connected to live tools (like the internet).
2. Why can’t the model be trained on live data all the time?
Training an LLM on new data is a massive, resource-intensive process. It involves:
Collecting data: Crawling the internet, curating information, and filtering for accuracy, relevance, and safety (e.g., filtering out harmful or biased content).
Training the model: Using powerful hardware (like GPUs or TPUs) and huge datasets to update the neural network. This takes weeks or months, even on the best systems.
Validating the model: After training, the LLM has to be tested to ensure it performs well and doesn’t produce harmful or nonsensical responses.
Because this process takes so much time and resources, training updates generally happen only periodically, not continuously.
3. Why do LLMs have "cutoff dates"?
Every time a model is trained, the specific data cutoff date is what defines what it knows. For example:
A model with a training cutoff of October 2023 would know about global events, technologies, and trends up to that month.
However, it wouldn’t know about events after that (e.g., it couldn’t know about news from February 2024 unless retrained or hooked to live data).
That’s why the cutoff date is baked into the AI's system prompt—it tells you what the model can "know."
4. What happens after the training cutoff?
After the cutoff date, the model:
No longer has access to new information.
Can still answer questions based on its training data, but guesses or hypothesizes about post-cutoff information if asked.
For example:
If the LLM’s training ended in October 2023 and you asked about a world event in March 2024, it wouldn’t "know." If it answered, it would essentially make educated guesses based on its knowledge.
5. How does this differ from tools connected to live data?
The key difference is live updates:
A standalone LLM depends entirely on its training data as of the cutoff date.
A connected tool (like ChatGPT with plugins or an AI-powered search like Firecrawl) fetches live data whenever needed, retrieving updated information from the internet.
This is why LLMs have training cutoff dates, but live tools can give you real-time information.