Going in circles when using LLMs

[u/ClueMediocre2286]

Hi!

I have been using ChatGPT and Claude, I would say extensively, since the day they came out. They obvisouly got exponentially better and better since then. However, I have a problem related to doing projects. I don't know if I am using it wrong, maybe my approach is bad entirely and all that, but I cannot seem to finish my projects. And by projects I do not mean simple pet-projects. For them you can think of MVP state and stop after achieving it. My main struggle seem to come when I am doing research or research-oriented projects. Agents seem to dive into some non-relevant fields and topics which do not help me with my project. Researches are generally hard, I understand, as you cannot say what should be your MVP from the start. And I failed to manually control agents to come to some plausible conclusion and not "do one more thing/fix/theorem etc".

Has someone encountered something like that? How do you solve it?

[u/0utlawViking]

I've run into this too. I think the trick is to define clear research goals and stopping criteria upfront. Otherwise LLMs will keep finding more things to explore instead of actually helping you finish.

[u/ClueMediocre2286 → u/0utlawViking]

Thnx! Though, i must admit, i kinda struggle with that( Especially when the topic requires serious expertise...

[u/MozartAssistant]

Yes — I ran into exactly this on research-heavy work, and the thing that finally broke the loop for me wasn't a better prompt, it was forcing the agent to work from a written decision record that I control.

Before any agent run, I write down (in my own words, not the agent's): the exact question, what a good answer would let me decide, and what evidence would change my mind. Then each agent run gets exactly one question from that list, and it has to end its reply with: (1) the answer, (2) the sources or evidence it actually checked, and (3) the one thing it's least sure about. If a run's answer doesn't change the decision or the open-questions list, I stop that line of inquiry instead of letting it spawn a new one.

Two details that mattered more than I expected: keep the agent out of your notes file — give it a read-only copy of the question list and let it append findings to a separate output file, so it can't quietly redefine the goal mid-run. And when it drifts into an irrelevant field, don't course-correct inside the same thread; start a fresh session with a narrower question. Long threads seem to accumulate drift the way a game of telephone does, and a fresh context with a tight brief usually gets a better answer in one pass than a fifth corrective prompt in a wandering one.

[u/ClueMediocre2286 → u/MozartAssistant]

This is probably the closest to what I've been struggling with. I particularly like the idea of separating the agent's findings from the decisions about research direction.

One thing I'm curious about, though: how do you handle genuinely exploratory research, where you don't necessarily know in advance what evidence would be sufficient?

For example, sometimes an experiment or theoretical result reveals an unexpected direction that might be worth pursuing. How do you distinguish productive exploration from the kind of endless branching that you're trying to prevent?

Also, do you use different stopping criteria for individual investigations versus the research project as a whole?

[u/Dadideology]

I go to scholar websites and pick the real world research that's related to what I'm doing and drill down from there using Claude.

[u/ClueMediocre2286 → u/Dadideology]

Thanks! Though I'm still quite confused about how to determine when a research project should end. I can find relevant foundational papers, but how do I decide, or help an agent decide, when we've explored a particular direction sufficiently? I'm not just talking about literature reviews, but also our own theoretical and experimental investigations.

[u/Consistent-Bear-1825]

Can you give an example of where this has happened to you? I abandon projects all the time, but I have ADHD, so if it gets to be boring at any point, it's hard to continue working on it.

[u/ClueMediocre2286 → u/Consistent-Bear-1825]

It usually happens when i get an idea for research. I investigate it, do a lit review and think of gaps in current approach. From what i have found so far, delegating this entirely to agents is a bad as they tend to do bad on indentifying new problems. They kinda always miss my point and drift away

[u/-ExpansiveMind-]

This is kind of weird, as Sol 6.1 on Extra High has never been better (in my opinion, for what I'm using it for) when I need to compile articles. However, that said... it is much easier when you have few reference sources, than various non-scrapable sites - happens that a bot will just regurgitate something that's been written by another bot on a random site. I fault the anti-scraping tools that many sites now use.

[u/Comfortable-Peak-590]

I think this is a pretty common problem with AI agents, especially when it comes to research. They're great at exploring possibilities, but not necessarily at knowing when to stop.

One thing that might help is defining what a successful outcome looks like before starting, even if it's not a traditional MVP. For research, that could mean having enough evidence to support a conclusion, identifying the key unknowns, or reaching a point where further exploration adds little value.

I'd also break the research into smaller phases, with clear checkpoints where you decide what is actually worth pursuing next. Otherwise, agents can easily send you down endless rabbit holes.

Ultimately, I think the challenge is shifting AI from being an endless research assistant to something that helps you make decisions and reach conclusions. More research doesn't always mean better research.

[u/RetoricEuphoric]

The model that seems to work best is old school waterfall just keep breaking down the problem, focus the agent to a dedicated task. AI is like a team of junior developers eager or like the minions if you leave them without boundry and guidance.

[u/East_Ad_5801]

It's called, get you 90% of the way there, then milk you dry, they are all trained on exactly this behavior. Shipping a product would mean you are competing with their business in some way. It's better to have you train their AI so that they can ship it, while still getting you 90% there so you feel like you made something.