yo, i feel u on the lazy output... i've been using DeepSeek Coder V2 for a bit and honestly, it was kinda disappointing at first. it kept skipping crucial blocks and giving me that 'any' junk in typescript. reallyyy annoying when you're trying to keep things modular and clean without wasting tokens. here's what i recommend to stop wasting money on retries:
* dont just say 'expert', use a list of 'Hard Constraints' in the prompt.
* explicitly state: 'strict typing only, NO any'.
* tell it to 'output full production-ready code blocks'—it helps with the skipping. tbh i had issues with it being too lazy until i added 'include all error handling' to the list. anyway, since DeepSeek R1 is so cheap (basically $0.55 per 1M tokens), getting it right the first time is the best way to stay under budget. gl!

similar situation here - i went through this last year when i was refactoring a massive FastAPI backend and it was honestly a total headache at first. i kept hitting that same wall where the model would just get lazy and skip error handling or give me generic 'any' types that i had to fix manually. it was super frustrating cuz i knew the logic was there, but the execution was just... off, you know? sooo i started playing around with my system prompt for DeepSeek Coder V2—which you mentioned earlier—and found that treating it like a 'Senior Staff Engineer' with a very specific 'Definition of Done' worked wonders. i basically moved away from vague descriptions and started using a structured XML-based setup. i tell it to always output a block before the actual code. it forces DeepSeek-R1 to actually think through the dependency graph before it starts typing. i also specify that it should prioritize 'explicit over implicit'—basically forcing it to write out those error blocks instead of skipping them. also, for the TypeScript stuff, i added a hard rule in the prompt: 'Strictly use interfaces, no type aliases for objects, and zero any usage.' if it breaks the rule, i have a pre-written 'critique' prompt i just fire back. i've been using the Continue.dev extension in Visual Studio Code to manage these system instructions and it's been a total game changer for my workflow. honestly, once i started providing a 'context skeleton'—basically a list of existing project patterns—the output became production-ready almost instantly. i think the model just needs those rigid guardrails to stop it from taking the easy way out. anyway, thats just been my journey with it, but sticking to those constraints realy saved my sanity!! gl with the microservices stack, sounds like a fun project. 👍

👆 this

Long system prompts eat tokens and costs add up fast! I'd actually suggest a different approach—keep it short for DeepSeek Coder V2. Just say 'no placeholders' at the end... literally amazing and so cheap!! idk

Honestly, i totally agree that those generic 'Senior Engineer' labels are losing their edge as these models evolve—theyve basically become noise in the context window. From a market research perspective, we’re seeing a shift from 'Role-Play' prompts to 'Structural' prompts. If you look at how DeepSeek-R1 handles logic compared to something like OpenAI o1-preview, the architecture responds way better to architectural constraints than social ones. Heres a quick breakdown of what actually moves the needle for high-level FastAPI/Next.js work: * **Interface-Driven Development (IDD) Prompting**: Instead of just saying 'be modular', tell it to 'map all Pydantic models to TypeScript interfaces before generating the route logic'. This forces a schema-first approach that eliminates those 'any' types youre seeing.
* **Pre-execution Verification**: Use a 'Double-Pass' instruction where you ask it to 'verify the AST compatibility of the imports'. DeepSeek Coder V2 is surprisingly good at this compared to Meta Llama 3.1, which tends to hallucinate library versions more frequently. Basically, if you stop treating it like a person and start treating it like a compiler-linked agent, the output quality spikes. The R1 model specifically loves 'state-driven reasoning'—ask it to 'identify potential edge cases in the middleware' before it touches the main controller logic. It adds a bit to the latency but saves you like 20 minutes of refactoring later.

Wow ok that changes things. Gonna have to rethink my approach now.

I’ve been messing with these LLMs for a while and honestly I’d skip the professional prompt engineering services and just build your own library of system prompts. The best DIY approach for DeepSeek is creating a self-correction pipeline rather than just a personality description. > I’m curious if there are specific keywords or structures that DeepSeek responds to better than others. I found that using a scratchpad or a thinking space before the code block works wonders for avoiding that lazy skipping behavior you mentioned. Basically you want to force the model to talk through the logic in its own words before it even touches the keyboard, right? It is basically a self-service way to ensure the model actually understands the architecture before it starts spitting out Next.js components and logic. I usually keep a few DIY prompt templates in Obsidian or Notepad++ and just swap them out based on the language. Here is what I usually include in my structure:

• a required breakdown of all state changes
• a section specifically for listing all necessary imports
• a mandatory edge case analysis block It definitely takes a bit more tokens to get that reasoning out in the open but it saves me so much time in debugging later on. Honestly I think these models just need a little nudge to realize they aren't supposed to take shortcuts, you know?

> I really want to get to a point where the first output is production-ready without me having to fix indentation or missing imports. I am so glad someone finally said it!! I have been struggling with this exact same thing for weeks and it is driving me absolutely crazy. I was trying to build a little app to track price drops for the Logitech MX Master 3S Wireless Mouse because i am trying to be super careful with my budget, but DeepSeek just keeps giving me these lazy, half-finished blocks. It is honestly so heartbreaking to watch my API credits drain away while i am basically begging it to just finish a script! I love how fast the R1 model is, it is truly amazing when it actually works! I love it for the logic but the amount of times i have to hit regenerate because it skipped a crucial import or an error handler is just painful. It is such a bummer because i thought i was being savvy by switching to a cheaper model to save some cash, but now i am just wasting tokens on broken code. The logic can be fantastic when it hits the mark, but the laziness is just killing me... i totally feel your pain on the indentation too. It is just so frustrating when you are so excited to see the output and then it just trips at the finish line!!