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A vector embedding is a numerical representation of data, think of it as translating words, images, or any piece of content into a list of numbers (a vector) that captures its meaning.
RAG (Retrieval-Augmented Generation) fixes this by injecting the right context into the prompt at query time, instead of baking it into the model at training time.
If the same prompt is sent multiple times, reuse the previous response instead of calling the API again.
The rise of AI agents has shifted how we build software. Instead of just APIs, we now design context-aware systems that can communicate intelligently with LLMs.
You can write perfect prompts and still get bad outputs — if you don't understand these two parameters.
If you've been treating AI prompts like Google search queries, you're leaving 80% of the value on the table.
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