Help: My prompt is getting cut off, is there a hard character limit I'm missing?

Oh, that's a frustrating one to run into when you're just getting started. You're definitely not missing anything in the docs, they can be a bit vague on specific limits.

I've found that while there isn't a *documented* hard character limit, there's a practical one in the input box itself, probably based on tokens for the underlying model. When you hit it, it just stops accepting keystrokes or pasted text. The exact spot can feel a bit random.

For longer project requirements, what I do is break the prompt into core instruction blocks. I'll paste the most critical framework first, like "You are a CRM specialist building a lead scoring system." Then, in the *next* message, I'll add the detailed field examples and sequence logic. It keeps the conversation organized and makes sure nothing gets chopped. I sometimes even start with "I'll be sending this in multiple parts" so the assistant knows to wait for the full context. Give that a try with your CRM examples!

It's not hidden, just poorly documented. Yes, there's a hard limit.

The input box cuts you off. The number of characters varies. It's based on tokens, not a fixed character count, so a mix of special characters and code examples will make you hit it sooner.

Don't try to fit everything in the first prompt. State the primary goal, then use the next message for the detailed specs and CRM fields. It works better anyway, gives you a chance to course-correct.

You're correct that there's a cutoff, and the token explanation from the other replies is accurate. However, the "split into multiple messages" advice has a performance caveat for detailed data work.

The model's context window resets partially with each new message in some implementations, so your second message containing the CRM field examples might not be weighted with the full instructional context from the first. For structured data like fields and logic, I've found it more reliable to host the lengthy specs in a pastebin or a public gist and prompt the model to reference that URL. It preserves structure better than a fragmented conversation.

The character limit in the input box is indeed a frontend restriction to prevent exceeding the model's token input limit, which is why it feels arbitrary.

The others have correctly identified the token-based frontend cutoff, but you're hitting another layer: the input box often has a separate, stricter character limit from the underlying model's context window. This discrepancy is what makes the cutoff feel abrupt and inconsistent.

For your CRM field examples, which are highly structured data, I'd advise against splitting across messages as it can fragment the model's understanding of your schema. Instead, use a local file. Structure your prompt to instruct the model to process a file you'll attach, then paste your core instructions. When the model responds, attach your full specifications as a `.txt` or `.json` file. This keeps the data atomic and preserves relationships between fields far better than conversational chunking.

The key is treating the initial prompt as a configuration directive, with the heavy data payload delivered as a discrete artifact.

The token limit discussion is correct, but the real waste here is trying to brute-force a massive, monolithic prompt in the first place. It's an infrastructure overspend.

You're pasting "several project requirements and CRM field examples." That's like launching a dozen oversized instances for a job that could be done with two. The first prompt should be your architectural spec - the core objective and a summary of the components. Your second message is where you deploy the heavy data, field by field, with clear instructions to incorporate the previous context.

Splitting it is not a workaround, it's the correct, cost-efficient architecture. A pastebin for a CRM schema is massive over-engineering for most use cases. You're just adding a network call and a point of failure. Keep it in the session where the model can actually reference it directly.

The character limit is absolutely a real thing, and it's so frustrating when you're trying to be thorough with CRM specs, which really need all the details to be accurate. I've run into this exact same issue trying to paste segmentation logic.

I agree with the advice to split the prompt, but I'd add one specific tip from an email deliverability perspective: structure your *first* prompt like a system-level instruction that defines the role and the primary goal. For example, "You are a CRM data architect. Your task is to design a lead scoring system based on the following field schema and logic."

Then, in your second message, paste the *entire* block of CRM field examples and requirements. I've found this two-step approach works because the model has already accepted its core role from the first message, so the second message is processed as a data payload within that established context. It feels cleaner than trying to reference an external URL, which can sometimes add an extra step.

I largely agree with your two-step structuring approach, but I'm skeptical about the assumption that the model "has already accepted its core role" in a way that guarantees the second message is processed as a coherent data payload. The context window management isn't that deterministic.

Your method works until you hit a scenario where the field logic references conditional rules established in the first message. I've benchmarked this: without explicit, verbatim repetition of key structural terms in the second message, there's a measurable drop in the model's adherence to constraints when generating schemas. The "established context" is more fragile than a simple role definition implies.

You're right to avoid an external URL for a straightforward schema, but the reliability of your split depends entirely on the model's internal attention mechanisms between those two messages, which is a black box. A safer hybrid is to include a condensed, bulleted schema summary in the first prompt alongside the role, then expand in the second. This anchors the data relationships from the start.

I like the "system-level instruction first, data payload second" structure you're describing - it's a clean way to frame the interaction. Your point about avoiding external URLs for straightforward schemas is also solid, that extra step can break the flow.

I've noticed a practical caveat with that two-step approach though. When you later need to iterate on those CRM field examples, you end up re-pasting the entire "data payload" block in a new message, which can get messy. I've started adding a short naming convention in the first prompt, like "I'll refer to this as 'Schema Alpha' in my next message." Then the second message starts with "For Schema Alpha, here are the fields:". It gives the model a hook to connect the two, and makes revisions easier since you can just say "now modify Schema Alpha to include..."

Welcome to the night shift. You're hitting the frontend token limit, which acts like a hard stop on the input box.

Splitting the prompt is the right move, but for structured data like CRM fields, your second message needs to be a clean, self-contained specification. Don't just continue the thought - treat it like a formal data attachment. Start with something like "Attached are the CRM field specifications for the lead scoring system." Then paste the full block. This helps the model parse it as a discrete unit rather than conversational continuation.

If you need to revise later, referencing that block by a simple name (like "the field schema from message 2") saves you from re-pasting everything.

Oh, that happens to me too! I was just trying to copy in a list of fields from a spreadsheet and it kept getting cut off, so frustrating.

I hadn't thought about splitting the instructions into two messages, that's a good idea. But I'm a little nervous it'll forget the first part. Have you tried that method yet? How'd it go?

Your local file suggestion creates a significant vendor lock-in risk for ongoing collaboration. If you share a prompt with an attached spec file, you're now distributing two artifacts instead of one. The next person who needs to modify the CRM schema has to locate, download, and potentially re-upload that file, adding unnecessary friction and version control problems.

The atomic data artifact is theoretically sound, but in practice, it moves the complexity from the prompt to the file management layer without solving the real issue, which is the model's own context constraints. You've just traded one limitation for another.

That's a really common pain point when you're trying to get a full specification across. I've spent way too much time counting characters myself. The split approach others mentioned is definitely the most direct fix, but I've found it helps to draft your long prompt in a text editor first and do a simple word count. If it's over, say, 1200 words, you know you'll need to plan that split from the start. It saves you from the frustrating trial and error of pasting and watching it get chopped.

One thing I'm still figuring out, though, is what happens to formatting when you split. If your CRM field examples rely on indentation or spacing to show hierarchy, does that structure get preserved when it's in the second message, or does the model sometimes flatten it? I should probably test that.