By carlc, product marketing manager at Filestack. By carlc, product marketing manager at Filestack. I remember the day I first started experimenting with DeepSeek for coding. It felt like stepping into a new dimension where code could almost write itself. At the time, I was juggling multiple projects, and I needed a way to streamline my workflow without sacrificing quality. DeepSeek for coding That was when I discovered how DeepSeek can assist with generating code snippets, entire functions, or even small modules. It did not eliminate my job as a developer, but it certainly made me more efficient. Now, after months of using it, I want to share some tips and tricks for crafting good prompts to get the most out of this approach. My Initial Impression When I began, I was both excited and a bit skeptical. Would the generated code be reliable? Could it handle tricky parts of my projects, like edge cases or new libraries? Yet, over time, I noticed that DeepSeek worked best when I gave it clear and precise instructions. If I typed a vague request like, “Create a function to parse user data,” the result would often be too general or miss the nuances of what I needed. However, if I typed something more specific, such as, “Create a function in Python that reads a JSON file with user data and returns a dictionary of valid users, ignoring entries with missing email fields,” the result was surprisingly accurate. I also discovered that context was crucial. If I asked DeepSeek for code without explaining what I wanted to achieve or without offering some background, it would guess based on limited data. For example, if I wanted a JavaScript snippet for a front-end web form, but never mentioned that it was for a browser environment, the output might not match my setup. On the other hand, once I specified my environment, language, and the nature of the task, I got code that was closer to what I actually needed. Why Prompt Clarity Matters One of the biggest lessons I learned is that DeepSeek is only as good as the instructions I give it. When I say instructions, I mean the descriptions I type in before hitting enter. It behaves almost like a conversation partner. If I supply well-structured instructions, it responds with code that aligns with my goals. However, if I send short or unclear requests, the results might not be very useful. Therefore, I start every prompt by stating the programming language I want. Then, I describe the purpose of the code. Next, I give more details, like any libraries or frameworks I plan to use, as well as important constraints. Finally, I mention the desired output or behavior. This approach ensures that DeepSeek understands the full picture and can generate something close to my ideal solution. Components of an Effective Prompt I like to think of an effective prompt in four main parts: Language and Environment: For instance, “Write a JavaScript function that runs in a Node.js environment” or “Generate a Python class for data analysis.”


Goal or Task: This might be “to analyze sales data” or “to handle user input in a React form.”


Constraints: Here, I mention anything special, such as, “The script should only use built-in Python libraries,” or “No external packages allowed.”


Desired Output: Finally, I clarify the exact outcome, like “Return a sorted list of user IDs” or “Log each error to the console.” Language and Environment: For instance, “Write a JavaScript function that runs in a Node.js environment” or “Generate a Python class for data analysis.” Language and Environment: For instance, “Write a JavaScript function that runs in a Node.js environment” or “Generate a Python class for data analysis.” Language and Environment Goal or Task: This might be “to analyze sales data” or “to handle user input in a React form.” Goal or Task: This might be “to analyze sales data” or “to handle user input in a React form.” Goal or Task Constraints: Here, I mention anything special, such as, “The script should only use built-in Python libraries,” or “No external packages allowed.” Constraints: Here, I mention anything special, such as, “The script should only use built-in Python libraries,” or “No external packages allowed.” Constraints Desired Output: Finally, I clarify the exact outcome, like “Return a sorted list of user IDs” or “Log each error to the console.” Desired Output: Finally, I clarify the exact outcome, like “Return a sorted list of user IDs” or “Log each error to the console.” Desired Output When I follow this structure, DeepSeek tends to produce code that fits my project requirements from the start. It also reduces the need for heavy edits after the code is generated. Furthermore, each of these steps prompts me to think carefully about what I really need, preventing me from sending half-baked requests. Iterative Refinement Despite my best efforts, I rarely get perfect code on the first try. However, one of the strengths of using DeepSeek for coding is that I can take the generated code, review it, run it, then refine my prompt. For example, if the code includes extra functionality I do not need, I just say, “Remove the data logging part and focus on the sorting mechanism.” If the code is missing a key step, I might say, “Please include a validation function for empty fields.” This iterative process feels like collaborating with a junior developer who writes an initial draft. I do not expect the first pass to be flawless, but I rely on the back-and-forth exchange to polish the code to my standards. Moreover, I always try to provide direct feedback about what went wrong or what could be better. If I simply tell DeepSeek to “Try again,” it might not know what to change. But if I say, “Please add error handling for file-not-found exceptions,” that usually gets me what I need. iterative process Common Pitfalls and How to Avoid Them Although DeepSeek can be quite powerful, there are a few pitfalls I have encountered: Lack of context: If you do not specify the environment or the programming language, you might end up with code in a language you did not want. Always state your language and context clearly.


Overly broad prompts: Vague prompts lead to incomplete or irrelevant results. Add detail by specifying your main goal, the libraries you plan to use, and any special constraints.


Ignoring best practices: Sometimes the generated code follows older conventions or might not follow your team’s style guide. It is important to review and adapt the code to ensure it meets your standards.


Missing error handling: In many cases, the generated code will not handle exceptions or invalid data by default. Including a requirement for error handling in your prompt can prevent major problems down the road. Lack of context: If you do not specify the environment or the programming language, you might end up with code in a language you did not want. Always state your language and context clearly. Lack of context: If you do not specify the environment or the programming language, you might end up with code in a language you did not want. Always state your language and context clearly. Lack of context Overly broad prompts: Vague prompts lead to incomplete or irrelevant results. Add detail by specifying your main goal, the libraries you plan to use, and any special constraints. Overly broad prompts: Vague prompts lead to incomplete or irrelevant results. Add detail by specifying your main goal, the libraries you plan to use, and any special constraints. Overly broad prompts Ignoring best practices: Sometimes the generated code follows older conventions or might not follow your team’s style guide. It is important to review and adapt the code to ensure it meets your standards. Ignoring best practices: Sometimes the generated code follows older conventions or might not follow your team’s style guide. It is important to review and adapt the code to ensure it meets your standards. Ignoring best practices Missing error handling: In many cases, the generated code will not handle exceptions or invalid data by default. Including a requirement for error handling in your prompt can prevent major problems down the road. Missing error handling: In many cases, the generated code will not handle exceptions or invalid data by default. Including a requirement for error handling in your prompt can prevent major problems down the road. Missing error handling By staying aware of these pitfalls, I save time and avoid having to rewrite large portions of the code. These days, I rarely forget to mention which environment I am working in because I have already seen how that can lead to confusion. Testing and Validating the Generated Code Once I get a piece of code from DeepSeek, I never assume it is fully correct. Instead, I test it rigorously, just like any other code I write by hand. I also check for security issues, performance bottlenecks, and logical errors. While DeepSeek helps me code faster, I still need to be the one who ensures the code behaves as expected. A simple process I follow is: Run the code in a safe environment (like a local sandbox) to make sure it does not break anything critical.


Add unit tests that cover different scenarios, including edge cases.


Check compatibility with my existing project structure and libraries. Run the code in a safe environment (like a local sandbox) to make sure it does not break anything critical. Run the code in a safe environment (like a local sandbox) to make sure it does not break anything critical. Run the code in a safe environment local sandbox Add unit tests that cover different scenarios, including edge cases. Add unit tests that cover different scenarios, including edge cases. Add unit tests Check compatibility with my existing project structure and libraries. Check compatibility with my existing project structure and libraries. Check compatibility If any step fails, I return to DeepSeek with new instructions, guiding it to fix errors or improve performance. This testing loop helps me stay confident in the final product. Using DeepSeek in Team Settings Over the past few months, I have also introduced some team members to DeepSeek for coding. Collaboration becomes easier when we share the prompts we used. For instance, if a teammate likes how I generated a particular database query, I can show them my exact prompt and instructions. This way, they can replicate my success without guessing how I did it. However, it is also important to maintain consistency across the team. We often decide on a standard format for prompts, so that everyone follows a similar approach. This ensures that the code we generate is more uniform and does not vary wildly from one developer to another. It also speeds up learning because newcomers can see a clear pattern in how we communicate with DeepSeek. Final Thoughts and Next Steps In my experience, using DeepSeek for coding can significantly reduce repetitive tasks and speed up many parts of the development process. Nevertheless, it is not a magic wand. Good prompts are the secret ingredient for receiving helpful code. Writing prompts that specify language, context, goals, and constraints is the best way to achieve high-quality results. Then, iterating and refining the code helps polish it into something ready for production. I believe that as more developers start using DeepSeek, we will see innovative ways to craft better prompts. We will also see new tips and tricks that help us refine code generation even more. As I continue working with DeepSeek, I plan to keep track of any new insights I discover. My hope is that this guide gives you a strong foundation to explore code generation without feeling overwhelmed. With clear prompts, careful testing, and a willingness to refine, you can unlock a faster, more efficient coding process that frees you to focus on the creative side of development. Remember, the key is not to expect perfection on the first attempt. Instead, think of DeepSeek as a coding partner that needs clear instructions and consistent feedback. Over time, you will find the perfect balance between human creativity and AI-driven efficiency. Happy coding, and may your prompts lead you to the cleanest, most elegant solutions possible! This article was originally published on the Filestack blog. This article was originally published on the Filestack blog. This article was originally published on the Filestack blog.

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