Mohd Kaif KaifAhmad1

Semantica: From RAG to Compiled Knowledge Systems

Inspired by Andrej Karpathy’s “LLM Wiki” idea.

What We’re Building

We’re building Semantica — a semantic layer that transforms unstructured data into persistent, structured knowledge graphs with reasoning and provenance.

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

	import os
	from google import genai
	from google.genai import types

	client = genai.Client(api_key=os.getenv("GEMINI_API_KEY","xxx"))

	# Repalce with the youtube url you want to analyze
	youtube_url = "https://www.youtube.com/watch?v=RDOMKIw1aF4"

	# Prompt to analyze and summarize the Youtube Video