Uploaded April 2026 | Updated September 2026, 2 hours ago
When you paste your company data into ChatGPT, you are not retraining the model. You are giving it temporary context for that conversation.
Same with RAG and embeddings: you are not injecting knowledge into the model’s brain, you are giving it access to external memory it can search when needed.
Training is the part that actually changes the model’s internal weights and reshapes how it behaves.
That is why prompting is great for context, RAG is great for controllable memory, and training is best when you need the model to truly adapt to a new style, skill, or domain. Huge difference, and it matters a lot when you are deciding where to spend time and money.
Curious where fine-tuning fits in this stack?
I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#AIExplained #ChatGPTTips #RAG #short
When you paste your company data into ChatGPT, you are not retraining the model. You are giving it temporary context for that conversation.
Same with RAG and embeddings: you are not injecting knowledge into the model’s brain, you are giving it access to external memory it can search when needed.
Training is the part that actually changes the model’s internal weights and reshapes how it behaves.
That is why prompting is great for context, RAG is great for controllable memory, and training is best when you need the model to truly adapt to a new style, skill, or domain. Huge difference, and it matters a lot when you are deciding where to spend time and money.
Curious where fine-tuning fits in this stack?
I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#AIExplained #ChatGPTTips #RAG #short
