If you're enthusiastic about learning Machine Learning and Reinforcement Learning, you should definitely check out @rasbt YouTube playlists.
The teaching style is exceptional, and the explanations are backed by practical, easy to follow examples. The quality of the content level
Vibe testing; means casually trying an LLM app with a few prompts and judging it by feel. Basically I asked it 6 to 8 questions, answers looked good, so I think it works..
Ever thought about what “Large” means in Large Language Models?
As @rasbt defines it in his book: “Large” refers to two things;
1: The model’s size in parameters (billions of weights)
2: The massive dataset it’s trained on (trillions of words)
@py_poojaa RAG is not dead lol, basic RAG is just becoming outdated.
The focus is shifting toward agentic RAG, better retrieval, and smarter context management.
Fine tuning and long context models have their place, but they don’t eliminate RAG.
RAG is not dead, it’s evolving…
Why Persistence is important for AI application?
Without persistence, LLMs would lose all session history after every restart. By persisting thread data and user context in external databases, modern ai applications deliver seamless long term continuity.
#LLMs#ai
What is Persistence in LLM application?
We all know the base LLMs are stateless, they forgot everything once a request ends. Persistence is the technique of savings chat sessions or threads, user states or context into external databases..
#persistence#llms
4_Context Drift / Hallucination: Pushing too much irrelevant background text into the context window creates noise, increasing the chance of hallucinations or off-topic responses.
Why does Short Term Memory fail?
While short term memory for short back and forth conversations, it fails as chats get longer due to four main reasons:
Here are the four main reasons discussed in comment section:
#stm#ai#llms
High Compute & KV Cache Costs: Attention processing scales quadratically (O(N2)). Re-reading thousands of words of chat history for every single message makes inference slow and expensive.
2_Hard Limits & Truncation: Context windows have token limits (e.g., 8k, 32k, 128k tokens). Once a conversation gets too long, older messages are dropped or cut off, causing the AI to "forget" earlier instructions.
1_"Lost in the Middle" Effect: Self attention mechanisms focus heavily on the beginning (system instructions) and the end (your latest question) of the context window. Critical details hidden in the middle of a long chat often get overlooked.
@rasbt sir mentioned in the book “ Build a Large Language Model from scratch” LLMs can generate surprisingly coherent, human like text. But “understanding” in AI isn’t consciousness or true comprehension. It’s advanced pattern recognition and contextual processing..
#llms