#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
Choc Chip Banana Bread ✨🍌🤎
Ingredients:
* 3 large ripe bananas (360–380 g)
* 130 g unsalted butter (will reduce to \~115 g once browned)
* 180 g dark brown sugar (+ 1–2 tbsp for topping)
* 2 large eggs
* 250 g plain flour
* 1 tsp baking soda
* ½ tsp salt
* ½ tsp cinnamon
* 70–100 g dark chocolate, chopped
* Optional: chopped nuts
#dinner
@dishes
My Kind of Girl Dinner 😮💨🍚🥒
Ingredients:
For the salmon:
* 2 salmon fillets (skin removed, cut into cubes)
* 2 tbsp soy sauce
* 1 tbsp oyster sauce
* 1 tsp brown sugar
* 2 tsp minced garlic
* 1 tbsp sriracha
* ¼ tsp chili flakes
For the rice & toppings:
* 100 g sushi rice (cooked)
* 1 tsp apple cider vinegar
* ½ tsp salt
* ½ tsp sugar
* Pinch of chili flakes
* ¼ cucumber, thinly sliced
* ¼ small onion, thinly sliced
* Handful of edamame (cooked)
* Black & white sesame seeds, for topping
#dinner
@dishes