If you work with data for a living, you have probably noticed that large language models are everywhere right now. Teams are dropping them into workflows, using them for automated analysis, and building products on top of them. Understanding LLM limitations for data scientists is no longer a desirable skill. It is the kind of knowledge that separates good work from costly mistakes.
This post is not meant to scare you away from these tools. It is about making sure you use them with your eyes open.
Where the Hype Overshadows the Reality
Most people hear about what LLMs can do. They hear about impressive demos and viral use cases. What they hear far less about is the failure modes, and those failure modes matter a great deal in production environments.
Large language models are trained on huge datasets of text. They learn to predict the next token based on patterns in that data. That sounds straightforward, yet the implications of that process are significant. These models do not “know” things in the way a database knows things. They generate plausible-sounding text based on statistical patterns, and that distinction matters.
Research by Ji et al. (2023) found that hallucination is a persistent and widespread problem across nearly all natural language generation systems, including the most advanced LLMs. In that study, the authors reviewed over 100 papers and found that models regularly produce confident-sounding outputs that are factually incorrect. For a data scientist building a pipeline that depends on accurate outputs, this is a serious concern worth addressing from day one.
LLM Limitations for Data Scientists Start With Hallucinations
Let us talk specifically about hallucinations, because they are one of the most important LLM limitations for data scientists to internalize.
When a model hallucinates, it generates information that appears correct but lacks any real grounding. This happens even with the most capable models available today. The model is not deceiving you intentionally. It simply does not have a mechanism to distinguish between what it has learned and what it is fabricating on the fly.
Maynez et al. (2020) demonstrated this in detail in their research on abstractive summarization. They found that a considerable portion of model-generated summaries contained hallucinated content, even when the source material was provided directly to the model. That means giving a model a direct context does not fully eliminate the problem.
For data scientists, this has real consequences. If you are using an LLM to extract structured information from documents, summarize reports, or generate code, you need to build in verification steps. Otherwise, errors can propagate silently through your entire pipeline, unnoticed. The pipeline appears to be working. The outputs look reasonable. The problem only surfaces later, when someone checks the numbers.
The Training Cutoff Is a Bigger Problem Than Most People Realize
Another major issue is the cutoff for the training data. Every LLM is trained on data collected up to a certain point in time. After that point, the model has no knowledge of anything that happened. Furthermore, it will not tell you that it does not know. It will often produce an answer regardless.
This creates a real challenge for data scientists working in fast-moving fields. Financial data changes daily. Research findings evolve constantly. Regulatory requirements shift. If you are using an LLM to reason about current information without connecting it to a live data source, you are likely introducing errors that are difficult to catch.
Bommasani et al. (2021) describe this in their foundational paper on large language models, noting that models inherit the limitations of their training data in ways that are not always transparent to the end user. That opacity is exactly what makes it risky in professional settings. You cannot trust a model to flag its own ignorance, so you have to design your system with that assumption built in.
Benchmark Scores Are Not the Same as Real-World Performance
Here is something that even experienced practitioners can be caught off guard by. A model that scores well on a benchmark does not necessarily perform well in your specific use case.
Benchmarks measure performance on standardized tasks under controlled conditions. Your production environment is almost certainly not a controlled condition. The inputs your users send, the edge cases in your domain, and the ways your pipeline interacts with the model all differ from what any benchmark was designed to test.
Srivastava et al. (2022) illustrated this clearly in the BIG-Bench study, which tested a broad range of capabilities across many language models. Their findings showed significant performance variance across tasks, and they concluded that aggregate benchmark scores often mask important weaknesses in specific areas. As a result, high leaderboard performance can give you a false sense of security when you are deploying a model for a domain-specific task.
This point is worth sitting with. A model that ranks near the top of a public leaderboard may still fail frequently on the specific thing you need it to do. Benchmarks are a starting point for comparison, not a guarantee of production readiness.
LLM Limitations for Data Scientists Show Up Clearly in Production
When you move from experimentation to production, LLM limitations become far more consequential. In a notebook, a wrong answer is just annoying. In a deployed pipeline, a wrong answer can lead to poor decisions, wasted resources, or damaged trust with stakeholders.
Several limitations tend to appear especially often in a real environment. Consider context length first. Every LLM has a maximum number of tokens it can process per request. When your input exceeds that limit, information gets truncated. The model does not tell you it missed something. It simply works with whatever it receives, and the output reflects that missing context without any warning.
Beyond context length, consider consistency. LLMs are probabilistic systems. Given the same input, they can return different outputs on different runs. For data pipelines that need repeatable results, this is a meaningful challenge. You can reduce the variance through settings like temperature, yet you cannot eliminate it entirely.
Then there is numerical reasoning. LLMs are notoriously poor at arithmetic and precise quantitative tasks. Bender et al. (2021) make a broader point in their work, arguing that models built primarily on text pattern-matching are structurally limited for tasks that require formal reasoning. For data scientists who lean on LLMs for anything involving numbers, that limitation is not trivial. It shapes how you should design any workflow that depends on numerical accuracy.
What Good Practice Looks Like Going Forward
The goal here is not to avoid LLMs. The goal is to use them thoughtfully. Understanding LLM limitations for data scientists means building systems that account for those limitations from the very beginning.
Several principles hold up well in practice. Treat LLM outputs as a first draft, not a final answer. Build evaluation layers into your pipelines rather than bolting them on later. Always test on real examples from your specific domain before trusting benchmark scores to guide deployment decisions. Connect your models to verified, current data sources whenever the freshness of information matters to the task.
Beyond those principles, staying engaged with the research helps. The work on LLM behavior is moving quickly. New failure modes get documented regularly, and the field is still learning what these systems can and cannot do reliably. Even reading a few papers per quarter keeps your understanding current, whereas casual blog reading does not.
The data scientists who get the most out of these tools are not the ones who trust them without question. They are the ones who understand where the tools break, who design their systems accordingly, and who build in the checks that catch problems before they grow. That approach is increasingly what separates solid work from work that causes problems down the line.
If you take one thing from this post, take this. LLMs are powerful and genuinely useful in the right context. They are also imperfect in ways that are not always obvious from the outside. Your job as a data scientist is to close that gap through thoughtful evaluation, careful system design, and a genuine curiosity about where these tools fall short.
References
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Bommasani, R., Hudson, D. A., Aditi, E., et al. (2021). On the opportunities and risks of foundation models. Stanford University Center for Research on Foundation Models. https://arxiv.org/abs/2108.07258
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1906–1919. https://doi.org/10.18653/v1/2020.acl-main.173
Srivastava, A., Rastogi, A., Rao, A., et al. (2022). Beyond the imitation game. Transactions on Machine Learning Research. https://arxiv.org/abs/2206.04615
