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In the world of AI development, building great applications isn’t just about technical chops — it's about mastering the vibe of coding. Whether you're crafting dynamic Large Language Model (LLM) tools or deploying sleek Private Tailored Small Language Models (PT-SLMs), the right mindset and workflows are what separate polished products from half-baked prototypes. Here are the six essential skills you need to vibe at a higher level when coding AI applications:
1. Thinking: Logical, Computational, and Procedural Thinking
Before a single line of code gets written, strong thinking patterns lay the foundation. Logical thinking helps you structure algorithms and decision flows clearly. Computational thinking enables you to break big problems into manageable parts, while procedural thinking ensures that your steps are systematic and replicable.
Tip: Practice mapping ideas into flowcharts or pseudo-code first — this forces clarity before you dive into technical execution.
2. Applying: Prompt Engineering Skills
Prompt engineering is the new superpower for developers working with LLMs and PT-SLMs. Crafting effective prompts isn’t just about wording — it’s about understanding model behaviors, biases, and context. Good prompts can significantly boost model performance, while weak prompts waste resources and return inconsistent results.
Tip: Use prompt templates, experiment with chain-of-thought prompting, and always test variations systematically.
3. Checkpoints: Building in Chunks with Version Control
Complex AI projects can easily spiral out of control without careful management. The antidote? Building in chunks — small, modular pieces that can be tested and versioned independently. Integrating Git (or another version control system) into your daily workflow allows you to checkpoint your progress, roll back mistakes, and collaborate effortlessly.
Tip: Commit often, write meaningful commit messages, and branch early for features or experiments.
4. Debugging: Methodical Problem-Solving Approaches
Debugging is inevitable, but chaotic debugging is optional. Great AI developers treat debugging as a calm, structured process: replicate the problem, isolate the cause, test hypotheses, and document findings. Especially with AI apps (where errors might be subtle or statistical), a disciplined debugging approach saves countless hours.
Tip: Develop a checklist for diagnosing issues, from input data checks to model output validation.
5. Context: Providing Relevant Information to LLMs / PT-SLMs
LLMs and PT-SLMs only perform as well as the context they are given. A missing detail or irrelevant input can completely derail outputs. Mastering how you structure, filter, and enrich the context you pass to your models is crucial for achieving coherent, accurate results.
Tip: Practice building dynamic context windows, using retrieval-augmented generation (RAG) patterns, and prioritizing "just enough" information rather than flooding the model.
6. MVP & Features: Iterative Development Process
Trying to build the “perfect” AI app in one shot is a trap. Instead, focus on delivering a Minimum Viable Product (MVP) quickly, then add features through continuous iteration. Each loop — build, test, learn, adjust — brings new insights that a massive one-time launch would miss.
Tip: Ruthlessly prioritize features, and schedule user testing early and often to catch misalignments fast.
Final Thought
Mastering vibe coding isn't about doing more; it’s about doing smarter and more intentionally. Focus on these six skills, and you’ll not only build better AI applications — you’ll enjoy the journey a lot more too.