Start with the simplest lever
When an AI feature underperforms, teams often jump to fine-tuning. Frequently the faster fix is better prompting, clearer examples, or retrieval of the right documents. Fine-tuning is powerful but costs data work, evaluation effort, and ongoing maintenance.
What prompting and system instructions do well
Prompts steer tone, format, and task structure. They are cheap to change and easy to A/B test. For brand voice, output schemas, and tool-use instructions, prompting should be exhausted thoughtfully before heavier methods.
When RAG fits
If answers must reflect private or frequently changing knowledge, retrieve documents instead of baking facts into weights. RAG keeps sources inspectable and updates cheaper than retraining. It fails when retrieval is weak, so invest in chunking and evaluation.
When fine-tuning makes sense
Fine-tuning helps when you need consistent specialised behaviour that prompts cannot reliably enforce, such as a domain style, classification taxonomy, or structured extraction pattern across thousands of examples. You need labelled data, holdout tests, and a plan for model upgrades.
A decision path
Define metrics. Improve prompts. Add RAG if knowledge is the gap. Consider fine-tuning only with data and eval capacity. Revisit as foundation models improve—sometimes a newer base model plus prompts beats an older fine-tune. Lunar Wave recommends this order to protect budget and focus.
When you put these ideas into practice, keep a short notebook of what worked and what felt noisy. Patterns emerge quickly once you review a week of real use rather than a single impressive demo.
Readers across India and other regions face different bandwidth, device, and language contexts. Favour workflows that remain useful on a mid-range laptop and a stable but not perfect connection.
Lunar Wave will keep returning to fundamentals like this because durable skills outlast any single product launch cycle. Clear thinking beats tool chasing every time.
Share what you learn with a colleague or classmate. Teaching a concept in your own words is one of the fastest ways to notice gaps in understanding.
As always, verify important claims with primary sources and keep sensitive data out of public AI tools unless your organisation provides an approved workspace.
When you put these ideas into practice, keep a short notebook of what worked and what felt noisy. Patterns emerge quickly once you review a week of real use rather than a single impressive demo.
Readers across India and other regions face different bandwidth, device, and language contexts. Favour workflows that remain useful on a mid-range laptop and a stable but not perfect connection.
Lunar Wave will keep returning to fundamentals like this because durable skills outlast any single product launch cycle. Clear thinking beats tool chasing every time.
Share what you learn with a colleague or classmate. Teaching a concept in your own words is one of the fastest ways to notice gaps in understanding.
As always, verify important claims with primary sources and keep sensitive data out of public AI tools unless your organisation provides an approved workspace.
When you put these ideas into practice, keep a short notebook of what worked and what felt noisy. Patterns emerge quickly once you review a week of real use rather than a single impressive demo.
Readers across India and other regions face different bandwidth, device, and language contexts. Favour workflows that remain useful on a mid-range laptop and a stable but not perfect connection.