Asking a chatbot for your kundli details feels convenient, and the answer it gives will almost always look confident and specific. That confidence is exactly the problem.
What a language model is actually doing
A large language model like ChatGPT works by predicting the next most likely piece of text given everything it has seen before, based on patterns learned from huge amounts of training data. It does not have a calculator for planetary orbits running underneath it, and it is not looking anything up in an astronomical table unless it has been explicitly connected to one. When you ask it for a planetary position, a tithi, or a nakshatra on a specific date, it produces text that pattern-matches what a correct answer tends to look like. Sometimes that happens to be right. Often, especially for less common dates or less common questions, it is not, and there is no way to tell just from reading the answer.
Why this specific task exposes the gap
General astrology questions, like what a placement broadly means, play to an LLM’s strength: summarizing patterns in language it has seen many times. A specific numerical output tied to an exact date and location is a different kind of task entirely. It requires an actual computation, not a plausible-sounding description, and a model with no real ephemeris behind it has no way to reliably produce that number.
What a real calculation looks like instead
Dedicated astrology software, including the tools on this site, runs on an ephemeris, most notably the Swiss Ephemeris, which computes planetary positions from established astronomical models rather than generating text. That is the difference between a computation and a guess dressed up as one. For a full explanation of how this site’s own calculations are built and verified, see the methodology page.
A concrete example of where it breaks
Ask a general-purpose chatbot for the moon sign on a date when the moon happens to change signs partway through the day, and there’s a real chance it gives you the wrong one, or a right-sounding answer that doesn’t match a proper calculation. The moon moves through a sign roughly every two and a quarter days, so any date near a transition is close to a coin flip for a model that isn’t actually computing planetary longitude, only recalling what similar questions and answers tended to look like in its training data.
A simple test to run yourself
Ask the same specific question, a planetary position for one exact date, time, and city, twice in separate conversations, or rephrase it slightly. If the answers differ even though nothing about the underlying facts changed, that’s a direct sign the model is generating plausible text rather than running a calculation, since a real ephemeris-based tool gives the identical answer every time for the same inputs.
What actual calculation tools look like
Software built specifically for Vedic astronomy, whether a desktop program, a commercial package, or a site’s own backend, pulls planetary positions from an ephemeris and works from established formulas for ayanamsa, house systems, and tithi boundaries. None of that involves predicting text. The trade-off is that these tools are less conversational than a chatbot, you get a chart and numbers, not a chatty explanation, which is exactly why they’re accurate for the numbers while a general-purpose LLM is better suited to explaining what a placement means afterward.
The fair takeaway
None of this makes LLMs useless, they are genuinely good at plenty of things. Precise, date-specific Vedic astronomy just is not one of them, and it is worth knowing which tool to reach for depending on the question.