This article is part of a series on the Sens-AI Framework—practical habits for learning and coding with AI.
In “The Sens-AI Framework: Teaching Developers to Think with AI,” I introduced the concept of the rehash loop—that frustrating pattern where AI tools keep generating variations of the same wrong answer, no matter how you adjust your prompt. It’s one of the most common failure modes in AI-assisted development, and it deserves a deeper look.
Most developers who use AI in their coding work will recognize a rehash loop. The AI generates code that’s almost right—close enough that you think one more tweak will fix it. So you adjust your prompt, add more detail, explain the problem differently. But the response is essentially the same broken solution with cosmetic changes. Different variable names. Reordered operations. Maybe a comment or two. But fundamentally, it’s the same wrong answer.
Recognizing When You’re Stuck
Rehash loops are frustrating. The model seems so close to understanding what you need but just can’t get you there. Each iteration looks slightly different, which makes you think you’re making progress. Then you test the code and it fails in exactly the same way, or you get the same errors, or you just recognize that it’s a solution that you’ve already seen and dismissed multiple times.
Most developers try to escape through incremental changes—adding details, rewording instructions, nudging the AI toward a fix. These adjustments normally work during regular coding sessions, but in a rehash loop, they lead back to the same constrained set of answers. You can’t tell if there’s no real solution, if you’re asking the wrong question, or if the AI is hallucinating a partial answer and too confident that it works.
When you’re in a rehash loop, the AI isn’t broken. It’s doing exactly what it’s designed to do—generating the most statistically likely response it can, based on the tokens in your prompt and the limited view it has of the conversation. One source of the problem is the context window—an architectural limit on how many tokens the model can process at once. That includes your prompt, any shared code, and the rest of the conversation—usually a few thousand tokens total. The model uses this entire sequence to predict what comes next. Once it has sampled the patterns it finds there, it starts circling.
The variations you get—reordered statements, renamed variables, a tweak here or there—aren’t new ideas. They’re just the model nudging things around in the same narrow probability space.
So if you keep getting the same broken answer, the issue probably isn’t that the model doesn’t know how to help. It’s that you haven’t given it enough to work with.
When the Model Runs Out of Context
A rehash loop is a signal that the AI ran out of context. The model has exhausted the useful information in the context you’ve given it. When you’re stuck in a rehash loop, treat it as a signal instead of a problem. Figure out what context is missing and provide it.
Large language models don’t really understand code the way humans do. They generate suggestions by predicting what comes next in a sequence of text based on patterns they’ve seen in massive training datasets. When you prompt them, they analyze your input and predict likely continuations, but they have no real understanding of your design or requirements unless you explicitly provide that context.
The better context you provide, the more useful and accurate the AI’s answers will be. But when the context is incomplete or poorly framed, the AI’s suggestions can drift, repeat variations, or miss the real problem entirely.
Breaking Out of the Loop
Research becomes especially important when you hit a rehash loop. You need to learn more before reengaging—reading documentation, clarifying requirements with teammates, thinking through design implications, or even starting another session to ask research questions from a different angle. Starting a new chat with a different AI can help because your prompt might steer it toward a different region of its information space and surface new context.
A rehash loop tells you that the model is stuck trying to solve a puzzle without all the pieces. It keeps rearranging the ones it has, but it can’t reach the right solution until you give it the one piece it needs—that extra bit of context that points it to a different part of the model it wasn’t using. That missing piece might be a key constraint, an example, or a goal you haven’t spelled out yet. You typically don’t need to give it a lot of extra information to break out of the loop. The AI doesn’t need a full explanation; it needs just enough new context to steer it into a part of its training data it wasn’t using.
When you recognize you’re in a rehash loop, trying to nudge the AI and vibe-code your way out of it is usually ineffective—it just leads you in circles. (“Vibe coding” means relying on the AI to generate something that looks plausible and hoping it works, without really digesting the output.) Instead, start investigating what’s missing. Ask the AI to explain its thinking: “What assumptions are you making?” or “Why do you think this solves the problem?” That can reveal a mismatch—maybe it’s solving the wrong problem entirely, or it’s missing a constraint you forgot to mention. It’s often especially helpful to open a chat with a different AI, describe the rehash loop as clearly as you can, and ask what additional context might help.
This is where problem framing really starts to matter. If the model keeps circling the same broken pattern, it’s not just a prompt problem—it’s a signal that your framing needs to shift.
Problem framing helps you recognize that the model is stuck in the wrong solution space. Your framing gives the AI the clues it needs to assemble patterns from its training that actually match your intent. After researching the actual problem—not just tweaking prompts—you can transform vague requests into targeted questions that steer the AI away from default responses and toward something useful.
Good framing starts by getting clear about the nature of the problem you’re solving. What exactly are you asking the model to generate? What information does it need to do that? Are you solving the right problem in the first place? A lot of failed prompts come from a mismatch between the developer’s intent and what the model is actually being asked to do. Just like writing good code, good prompting depends on understanding the problem you’re solving and structuring your request accordingly.
Learning from the Signal
When AI keeps circling the same solution, it’s not a failure—it’s information. The rehash loop tells you something about either your understanding of the problem or how you’re communicating it. An incomplete response from the AI is often just a step toward getting the right answer. These moments aren’t failures. They’re signals to do the extra work—often just a small amount of targeted research—that gives the AI the information it needs to get to the right place in its massive information space.
AI doesn’t think for you. While it can make surprising connections by recombining patterns from its training, it can’t generate truly new insight on its own. It’s your context that helps it connect those patterns in useful ways. If you’re hitting rehash loops repeatedly, ask yourself: What does the AI need to know to do this well? What context or requirements might be missing?
Rehash loops are one of the clearest signals that it’s time to step back from rapid generation and engage your critical thinking. They’re frustrating, but they’re also valuable—they tell you exactly when the AI has exhausted its current context and needs your help to move forward.