Why Your AI Coding Assistant Is Failing (And How to Fix It)

Many developers are turning to AI assistants like ChatGPT and Claude to accelerate their coding workflows, but experience can be a mixed bag. It's common to face frustration when these powerful tools fail at seemingly simple tasks, such as correctly formatting a bash script, finding a missing brace, or remembering a specific instruction from earlier in the conversation. If you're getting inconsistent or incorrect code, the issue might not be the model's capability, but how you're using it. Several proven strategies can dramatically improve the quality and reliability of the code you generate.

Master Your Conversation Context

One of the most common pitfalls is using a single, long-running conversation for a complex project with multiple features. As the chat history grows, the context window becomes cluttered, which can lead to model instability, forgetfulness, and unpredictable behavior. The model can get confused by conflicting or outdated information from earlier in the thread.

The Fix: Instead of continuing a long conversation, start a fresh chat for each new, distinct task. Craft a new, comprehensive prompt that includes all the necessary context, requirements, and code snippets for that specific task. This provides the model with a clean slate, free from the noise of previous interactions, leading to more focused and accurate responses.

Level Up Your Prompting Game

Simply telling the AI what you want is often not enough, especially for languages with strict syntax like bash. You can significantly steer the model toward a correct output with more sophisticated prompting techniques.

Multi-shot Prompting: Go beyond a single request. Provide the model with several examples of a request paired with a perfect output. This technique, known as multi-shot prompting, shows the model exactly what you're looking for, reducing the chances of it generating malformed or incorrect commands.
The "Running To-Do List" Method: To combat the model's tendency to forget rules, create a persistent set of instructions. With Claude, you can use the "artifact" feature to maintain a running to-do list or a set of standing orders. Start your chat by defining these rules (e.g., "All scripts must display a version number" or "Always mark the end of a script with '#finish'") and instruct the model to consult this list for every change. This serves as an external memory, ensuring your requirements are consistently met.

Choose the Right Tool for the Job

Not all AI models are created equal, and performance can differ significantly based on the task, the model version, and the service tier.

Model Tiers Matter: Users often report that higher-tier models (like Claude 3 Opus) are far more capable and reliable for coding than their lower-cost counterparts (like Claude 3 Sonnet). If you're using a base-level paid plan and getting poor results, the problem may be the model's inherent limitations. Upgrading may be necessary for more demanding tasks.
Experiment with Different Models: The best model for one person's workflow might not be the best for yours. While some find success with ChatGPT and Claude, others have noted that Google's Gemini provides better results for their specific coding needs. It's worth trying different platforms to see which one aligns best with your style and tasks.
Work with Smaller Code Blocks: LLMs can struggle when fed a massive, monolithic script. Performance improves when you break your code down into smaller, more manageable modules or functions (a good rule of thumb is under 500 lines of code). This allows the model to focus on a specific piece of logic without being overwhelmed.