Honestly, stick to modular chunks to save those tokens. I use DeepSeek V4 Pro 128k context and it works well without breaking the bank. Super happy with the results so far.

Saw this earlier but just now getting a chance to sit down and type this out. Honestly, stop dumping the full codebase. You're wasting tokens and diluting the model's focus. I have been using DeepSeek V4 Pro 128k context for months and the trick is definitely in the system instructions. I find that forcing the model to explain its logic before writing the code helps a ton with accuracy. Try giving it a specific architectural overview first, then feed it only the relevant file paths or headers it needs. If you're on a tight budget, check out OpenRouter DeepSeek V4 Pro because their pricing is pretty transparent and helps me keep costs predictable throughout the month. I also find using Cursor AI IDE as a front-end for these prompts makes a huge difference since it handles the context window much smarter than just copying and pasting into a raw chat box. It keeps the token usage lean by only indexing what's actually necessary. Don't overthink the few-shot stuff until you've nailed down the system prompt instructions. Keep it concise, define your coding style requirements clearly upfront, and focus on one function at a time. It works well and I've had no complaints with the output quality once I switched to this modular, systematic approach.

Late to the party, but ive learned over the years that safety comes first. dumping code blindly usually leads to hallucinated dependencies. before i suggest a workflow, what kind of architecture are you building? specifically, are you using NVIDIA RTX 4090 24GB GDDR6X for local testing?

• decouple logic from boilerplate
• verify outputs with unit tests
• keep context constrained constantly feeding the model everything is just asking for errors.