What is the biggest problem with LLM?

The biggest problem with large language models is not raw intelligence or the novelty of their outputs. It is reliability. Founders often discover this the hard way, at the exact moment a glossy sales promise collides with production reality. In the demo, the model dazzles. It drafts confident emails, summarizes calls, and routes tickets as if it has spent years on your team. Once the pilot glow fades, the same system reveals what it really is, a probabilistic engine that predicts tokens. It does not owe you truth. It does not sign contracts. It will sing when you feed it the happy path and it will drift when you widen the task. That gap between promise and guarantee is where products break, margins evaporate, and trust thins out.

Modern teams still try to ship these models like traditional software. They expect repeatable behavior from a system that is, by design, variable. When variability is harmless, like a difference in tone or a synonym choice, nobody cares. When variability means invented policy language, incorrect figures, or a fabricated citation in a support reply, you are no longer looking at a cute glitch. You are looking at operational risk. The instinctive reactions are familiar. Make the prompt longer. Increase the temperature or decrease it. Buy a larger context window and stuff in more notes. None of these moves change the fundamental mismatch between a workflow that needs hard guarantees and a model that offers only likelihoods.

Reliability begins with the way you frame the job. Accuracy is not a single dimension. It is a bundle of decisions that should never be collapsed into one step. Classification, extraction, retrieval, and generation are different jobs with different risk profiles. A model that chooses a label can be graded and governed one way. A model that pulls facts from a library can be checked against sources. A model that fills a template with numbers can be audited deterministically. When a team hands one prompt the entire bundle and asks it to be both poet and accountant, they set themselves up for expensive corrections. The first act of a reliable product is to carve the job into deterministic rails and probabilistic spans, and then give the model a decision envelope. Inside that envelope, it can act. At the margins, it must seek evidence. Outside it, it must escalate.

This is not pedantry. It is how you protect your user’s time and your own unit economics. Teams routinely celebrate throughput because everything the model touches looks like productive work. The hidden cost is correction. A hallucinated paragraph can take five minutes to fix. A misrouted ticket can take hours of relationship repair with an enterprise account. A wrong dollar figure in a summary can trigger a legal review that nobody budgeted for. If your dashboard counts output but ignores correction cost, you will misread the health of the system. The only metric that matters over time is net contribution after correction cost. That is the measure that aligns reliability with margin, and it rarely flatters a product that relies on vibes.

Reliable systems are built around explicit contracts, not fuzzy intentions. Instead of promising general intelligence, promise bounded behavior that you can test. Promise that the assistant will never invent policy language. Promise that answers will come only from a defined corpus. Promise that uncertainty will be flagged rather than asserted as fact. These promises are not marketing slogans. They are design constraints. They force you to turn retrieval into a gate that must pass before generation runs. They push you to replace freeform extraction with typed schemas that the rest of your stack can validate. They encourage generation to become a series of slot fills inside a deterministic template rather than an open essay that your reviewers have to read like literature.

From there, build an evidence ladder that keeps the model honest. Do not throw the entire context window at the model and hope that the right passage wins a hidden attention lottery. Require the model to cite the snippets it used. Store those citations next to the output. Run simple checks that verify the presence of required entities and thresholds. Refuse to render a final answer when the supporting evidence is missing or contradictory. In those cases, produce a question for the user or a targeted request for more input. You are not slowing the workflow. You are trading false confidence for a short, recoverable turn that preserves trust.

Every reliable system needs a clean path for escalation. Treat the model as a first pass reviewer rather than a final author. Route uncertain work to a human or to a smaller deterministic program, but never route both in the blind. Give the reviewer structured state, including the original input, the retrieved evidence, the draft output, and the reason the model hesitated. With that context, a human corrector does not waste time rediscovering the problem. Review time falls, and your team generates labeled examples of borderline cases without asking anyone to become a formal annotator. These examples feed back into smarter routing and tighter envelopes, which in turn reduces the frequency of escalation.

Evaluation is where teams often drift into self-deception. A test set scraped from last month’s marketing deck will flatter your progress and betray you in the field. Build three small, stable, and ruthless suites. One should cover the high volume tasks that dominate your daily traffic. One should cover the high risk edge cases that can hurt a customer or expose the company to liability. One should cover the ambitious promises the sales team keeps making. Tie each suite to a hard gate in continuous integration. Do not ship a model, a prompt, or a retrieval change that fails any suite. When you enforce this discipline, your product stops being a mood and becomes a testable system.

Reliability does not happen by accident. It requires clear ownership and aligned incentives. If the person who designs prompts is celebrated for speed but never bears the cost of downstream corrections, you have created a structure that rewards fragility. If your product requirements read like aspirational tweets, your engineering team will ship clever hacks that look fine at ten customers and crumble at a hundred. Give a single leader end to end accountability for reliability, including the authority to slow a launch when the evidence is not there. Tie compensation to net contribution after correction cost and make that scoreboard visible. Culture follows the incentives that people see every day.

Pricing eventually exposes every shortcut. If you sell your product like deterministic software while paying for probabilistic retries and human edits, your gross margin will look decent when you are small and will deteriorate as you add customers. The fix is to meter uncertainty in your commercial model. Charge a premium for guaranteed jobs that stay inside tight envelopes and stable corpora. Discount exploratory jobs that include a review step. Offer better pricing when customers upload high quality documents and allow you to constrain answers to that set. Many buyers will accept the exchange because reliability is valuable to them and ambiguity is a cost they already carry in other parts of their operations.

Founders sometimes ask if they should wait for the next generation of models. It is tempting to think that a larger model or a wider context window will solve reliability. Better models help, but they do not remove the need for structure. A longer chain of thought does not replace a missing contract. A fine tuned instruction set does not cure a product that asks a system to do unbounded work with limited supervision. The teams that win are not those who predict the next benchmark leader. They are the teams who take messy language prediction and wrap it in a service layer that defines boundaries, demands evidence, and makes correction cheap.

If you want a practical starting line, take one user journey and split it into three layers. At the top, design the interface to promise only what you can test. In the middle, do retrieval and extraction that produce typed facts with citations. At the bottom, let generation fill narrow templates and refuse to invent new entities without evidence. Enforce an uncertainty budget that limits retries and triggers escalation when the budget is spent. Measure net contribution after correction cost for that one journey every week. When the trend improves, expand the envelope gradually. When it worsens, you have found the seam where the product is pretending to be deterministic. Adjust the contract, revise the envelope, or narrow the task before adding more traffic.

Reliability is not a decorative feature for an AI product. It is the operating system that governs everything else. Models will charm you in the lab and seduce your roadmap with possibilities. The market pays for guarantees. If you treat an LLM like a teammate without a contract, it will pull you into promises you cannot keep. If you treat it like a fallible service inside a disciplined system, it will earn its keep and compound your advantage. That is the real work of building with these tools, and it is the difference between a demo that raises a round and a product that deserves a market.