Struggling with hallucinated libraries is the worst, especially when you are under a tight deadline. I dealt with similar logic drifts last week and honestly, the model seems to lose its system prompt focus once the context window gets bloated with junk. You might want to consider dumping everything into a structured schema before you hit enter. I started forcing it to output in a specific JSON format and it cleaned up the syntax errors significantly.

• Define your internal API boundaries explicitly.
• Force the model to output a step-by-step verification plan before the code block.
• Use a strict zero-shot constraint for external dependencies. Be careful with how you feed it existing file structures. I found that using a Logitech MX Master 3S Wireless Mouse to quickly drag and drop my reference docs into a structured scratchpad helps me stay organized, but keeping your logic modular is what actually forces the model to stay grounded. Just keep it simple.

Honestly, I have had issues with DeepSeek V4 Pro for complex logic tasks lately. Its hallucination rate is honestly not as good as expected, especially when you start throwing multi-file architecture at it. It seems to struggle when the context window gets crowded, and unfortunately, it just makes up fake modules or deprecated APIs to fill the gaps. To get this behaving, you need to force a rigid structure. Here is what has been working for me:

• Define a step-by-step role: Start your prompt by explicitly stating that it is a senior engineer who prioritizes existing documentation over creative solutions.
• Use pseudo-code constraints: Tell it to write the logic in comments first, then implement the code in blocks. This forces the model to reason through the dependency graph before it starts writing syntax.
• Reference specific documentation: Paste the relevant API docs or man pages directly into the chat. It helps prevent those weird library hallucinations you are seeing. If you are doing heavy local debugging, you might want to look into DeepSeek DeepSeek-V3 671B if you can run it via an API endpoint, or just stick to a local sandbox. I usually run my scratchpad through VS Code Editor Version 1.95 with strict linting enabled, so it catches those fake libraries before they drive me crazy. Just keep your prompts hyper-modular; dont ask for a whole feature, just ask for one function at a time. It is a pain, but it is the only way to keep it focused.

> it just starts hallucinating random libraries that dont even exist Honestly, I feel that pain deep in my soul. Like someone mentioned, the context bloat is a massive issue with this model, and unfortunately, it really struggles to maintain logic integrity once the files start stacking up. I had issues with it last week on a backend refactor and it kept hallucinating legacy modules that simply arent real. Stop trying to force it to read your whole repo at once, thats a recipe for disaster. My experience? Keep the prompts modular. If you just dump a massive stack into the prompt, the model loses the plot and starts guessing. Its not as good as expected for large-scale architecture, so try feeding it one function at a time. Also, double check that you arent accidentally letting it use deprecated documentation from your search history. Most people just blindly trust the output, but you really gotta verify every single import statement or you'll be debugging ghost code all night. Honestly, just be paranoid about the imports and you might actually hit that Friday deadline.

Following