#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
⚡️Los Rays en sus últimas 4 series:
Barreda de 3-0 contra los Twins
Victoria de la serie de 2-1 contra los Guardians
Barreda de 3-0 contra los Giants
Barreda de 3-0 contra los Blue Jays
Tampa Bay está en una racha ganadora ☀️
#Rays
🗞 | t.me/MLB_Daily
⚡️🦐 Según se informa, el lanzador derecho Nick Martinez y los Rays han llegado a un acuerdo de contrato por un año.
#️⃣#Rays#NMartínez
🗞 | t.me/MLB_Daily
💥💱 𝑻𝑹𝑨𝑫𝑬 𝑨𝑳𝑬𝑹𝑻 💱💥
🦤 Orioles reciben a:
⚡️ RHP Shane Baz
⚡️ Rays reciben a:
⚾️ OF Slater de Brun (Prospecto #6 de Orioles)
⚾️ C Caden Bodine (Prospecto #10 de Orioles)
🧢 RHP Michael Forret (Prospecto #11 de Orioles)
⚾️ OF Austin Overn (Prospecto #30 de Orioles)
- una selección de la Ronda A de Equilibrio Competitivo
#️⃣#Trade#Orioles#Rays#Baz#DeBrun#Bodine#Forret#Overn
🗞 | t.me/MLB_Daily