Fix Common Gemini 2.5 Pro Coding Mistakes

8/14/2025

Getting Real About Gemini 2.5 Pro: Fixing the Coding Mistakes It Keeps Making

Hey everyone, let's have a real chat about Gemini 2.5 Pro. It's an incredibly powerful tool, no doubt. Google's latest model can feel like a glimpse into the future of software development, with its massive context window & impressive reasoning capabilities. But if you've been using it for any serious coding, you've probably also had moments where you want to pull your hair out.

Honestly, it can be a bit of a rollercoaster. One minute it's generating a perfectly elegant solution to a complex problem, & the next it's making a rookie mistake that leaves you scratching your head. It’s still an experimental technology, after all, & sometimes it shows.

I've been in the trenches with Gemini 2.5 Pro, using it for everything from quick scripts to architecting larger applications. & I've noticed a pattern of recurring mistakes. The good news is, most of them are fixable, or at least manageable, once you understand what's going on under the hood.

So, I wanted to put together a comprehensive guide on the most common coding mistakes Gemini 2.5 Pro makes, & more importantly, how to fix them. This isn't about bashing the tool, but about learning how to work with it effectively. Think of it as a field guide for navigating the quirks of your brilliant, but sometimes eccentric, AI coding partner.

The "I Know Better Than You" Syndrome: When Gemini Makes Unwanted Assumptions

One of the most talked-about frustrations with Gemini 2.5 Pro is its tendency to make assumptions about your code. You'll ask it to perform a specific task, like refactoring a function or adding a new feature, & it will take the liberty of "fixing" other things it deems incorrect.

A developer on Reddit shared a perfect example: they had a function to extract a score from a string, with a specific regex for a 1-10/10 format. When they asked Gemini to use that function to calculate something else, the AI decided the original regex was a "bug" & changed it to also accept 0/10, breaking the intended logic. That's not just unhelpful; it's actively counterproductive.

This "helpfulness" also manifests in excessive verbosity. Gemini loves to add tons of comments, muck around with your line spacing, & add play-by-play annotations for every little change. While it might think it's being thorough, it often just creates a lot of noise you have to clean up.

How to Fix It:

BE HYPER-SPECIFIC in Your Prompts: This is the golden rule. Don't just tell it what to do; tell it what not to do. For example, instead of "Refactor this function to be more efficient," try "Refactor this function for efficiency. DO NOT change the existing logic for input validation. Do not add any new comments or change the existing code formatting." It feels a bit like you're micromanaging an intern, but it works.
Break Down Complex Tasks: Instead of giving Gemini a huge, multi-step task, break it down into smaller, more focused prompts. This reduces the chances of it going off on a tangent. For example, instead of asking it to build an entire web page, ask it to generate the HTML structure first. Then, in a separate prompt, ask for the CSS, & then the JavaScript.
Set the Scene: Start your conversation by setting the context. Tell Gemini what your overall goal is, what the existing code does, & any constraints it needs to be aware of. The more context you provide, the less it has to guess.

The Amnesiac Developer: Context Window Issues & Repetitive Errors

Gemini 2.5 Pro boasts a massive 1 million token context window, which is incredible for analyzing large codebases. But here's the thing: it's not always as reliable as it sounds. Some users have found that it starts to get unreliable after around 200,000 tokens. It might forget previous instructions, lose track of the overall architecture, or even re-introduce bugs it has already fixed.

Another common complaint is that Gemini will fix a bug, but then in the next code generation, it will make the exact same mistake again. This can be incredibly frustrating, especially when you're in a flow & don't want to keep repeating yourself.

There are also reports of files getting "stuck" in its memory. A user on a Google AI Developers Forum described sending one file for analysis, then sending a second file later, only for Gemini to provide a summary based on the first file. This points to some challenges with how it manages context over a longer conversation.

How to Fix It:

Start a New Conversation: If you notice Gemini getting stuck in a loop or making repetitive mistakes, the easiest solution is often to just start a new chat. This gives it a clean slate & can often resolve the issue immediately.
Checkpoint Your Progress: For larger projects, one clever workaround is to have Gemini recursively write checkpoint messages for you. This way, you can reset the chat every 200k tokens or so without having to re-establish the entire context from scratch.
Manage Your Temperature Settings: The "temperature" setting controls the randomness of the output. For more creative or brainstorming tasks, a higher temperature can be useful. But for coding, especially when you need consistency, try lowering it to around 0.2.
Switch Models Temporarily: If you're experiencing context memory issues, one user found that switching to the Flash 2.5 model & then back again can help clear the memory.

When "Thinking" Gets in the Way: Latency & Performance Quirks

Google has said that the Gemini 2.5 models have "thinking" enabled by default to enhance quality. This means it's designed to take a more step-by-step reasoning approach, which can lead to better code. However, this can also result in higher latency & token usage. If you're trying to work quickly, waiting for Gemini to finish "thinking" can be a drag.

You might also run into issues with repetitive tokens, especially in structured formats like Markdown tables. This happens when the model gets stuck in a loop & keeps generating the same output over & over.

How to Fix It:

Adjust or Disable "Thinking": If speed is your priority, you can adjust or even disable the "thinking" feature. This might result in slightly lower quality output, but it can significantly reduce latency.
Be Specific About Formatting: If you're having issues with repetitive tokens in things like Markdown tables, add specific instructions to your prompt about how you want the output to be formatted. Providing examples can also help.
Increase Your Client-Side Timeout: Sometimes, slow responses aren't Gemini's fault, but rather a timeout issue on your end. Try increasing the timeout settings in your client to give the model more time to process your request.

The Dreaded API Errors: 429s, 500s, & More

If you're using the Gemini API, you've probably encountered your fair share of error codes. These can be anything from

429 Resource Exhausted

500 Internal Server Error

, & they can bring your development to a halt.

These errors often come down to a few common issues: exceeding your rate limits, problems with your API key, or temporary issues on Google's end.

How to Fix It:

Implement Exponential Backoff: This is a crucial strategy for dealing with rate limit errors (429s). Instead of just retrying immediately, wait a short, exponentially increasing amount of time between retries. This gives the API a chance to recover & increases your chances of a successful request.
Double-Check Your API Key & Configuration: Make sure your API key is correct, active, & that the Gemini API is enabled in your Google Cloud project. Also, ensure you're using the correct model identifier in your code.
Monitor the Gemini API Status Page: If you're seeing a lot of 500 or 503 errors, it's a good idea to check the official Gemini API status page for any ongoing outages or issues.

The Bigger Picture: Working Smarter with Gemini 2.5 Pro

Beyond fixing specific mistakes, there are some broader best practices that can help you get the most out of Gemini 2.5 Pro & improve the quality of the code it generates.

Start with Architecture: Use Gemini for high-level planning & design patterns before you start writing code. It's great at suggesting different architectural approaches & helping you think through the pros & cons of each.
Use a Hybrid Approach: For large, complex projects, don't rely on Gemini to do everything. Use its analysis capabilities to get insights & suggestions, but combine them with your own expertise & judgment.
Leverage Multimodality: One of Gemini's biggest strengths is its ability to understand different types of inputs. You can give it code snippets, error logs, & even screenshots of a faulty UI to help it diagnose problems more effectively.
Incorporate AI Chatbots for Better User Experience: When you're building applications with the help of Gemini, think about the end-user experience. For example, if you're creating a customer-facing website or application, you'll need a robust customer service solution. This is where a tool like Arsturn can be incredibly valuable. You can use Arsturn to build a no-code AI chatbot trained on your own data. This chatbot can provide instant customer support, answer questions about your product or service, & engage with website visitors 24/7. It's a great way to provide a personalized customer experience & boost conversions, freeing you up to focus on the core development of your application.

My Final Thoughts

Look, Gemini 2.5 Pro is an amazing tool, but it's not a magic wand. It's a powerful assistant that can supercharge your development workflow, but you still need to be the one in the driver's seat. By understanding its common mistakes & learning how to work around them, you can avoid a lot of frustration & unlock its true potential.

It’s all about learning the quirks & developing a feel for how to prompt it effectively. The more you use it, the better you'll get at guiding it towards the results you want. And as Google continues to iterate & improve the model, I'm optimistic that many of these rough edges will be smoothed out.

I hope this was helpful! I'm curious to hear about your own experiences with Gemini 2.5 Pro. What are the most common mistakes you've seen it make? And what are your best tips & tricks for getting it to produce high-quality code? Let me know what you think.