Renat Alimbekov's personal blog

Llms.txt is a special text file that allows websites to be understood more effectively by artificial intelligence systems and large language models. The file is placed in the root directory of the website and helps AI systems like ChatGPT, Google Gemini, Claude, and Perplexity process content more accurately.

Origin and Purpose

The llms.txt format was proposed by Jeremy Howard in September 2024 as a solution to the problem of HTML structure complexity for AI systems. Web content often contains complex structures, navigation menus, advertisements, and JavaScript, which makes it difficult for language models to understand the content.

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In today’s AI-driven world, Retrieval-Augmented Generation (RAG) is becoming an increasingly significant approach that combines the capabilities of information retrieval with the generative abilities of large language models (LLMs). This overcomes a number of limitations faced by traditional LLMs and provides more accurate and fact-based answers.

What is RAG?

RAG is not a single technology, but an entire umbrella of different components, designs, and domain-specific adaptations. A typical RAG system includes:

1. A data ingestion component: where data is processed, embedded, and stored as context documents in a vector database
2. A retrieval component: where context documents are retrieved and ranked for relevance to the query
3. Query component: where the prompt with the query is combined with the search results and sent to the LLM

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RouteLLM reduces your costs for using LLMs by 3.6 times.

It chooses whether to use a strong or weak LLM model depending on the complexity of the user’s query. This optimizes the balance between cost and quality of the response.

The Python library allows you to use this approach directly.

import os
from routellm.controller import Controller

os.environ["OPENAI_API_KEY"] = "sk-XXXXXX"
# Replace with your model provider, we use Anyscale's Mixtral here.
os.environ["ANYSCALE_API_KEY"] = "esecret_XXXXXX"

client = Controller(
  routers=["mf"],
  strong_model="gpt-4-1106-preview",
  weak_model="anyscale/mistralai/Mixtral-8x7B-Instruct-v0.1",
)

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Google DeepMind has developed a multi-pass online approach using reinforcement learning to enhance the self-correction capabilities of large language models (LLMs).

It has been shown that supervised fine-tuning (SFT) is ineffective for learning self-correction and faces a mismatch between training data and the model’s responses. To address this issue, a two-stage approach is proposed, which first optimizes self-correction behavior and then uses an additional reward to reinforce self-correction during training. This method relies entirely on data generated by the model itself.

When applied to the Gemini 1.0 Pro and 1.5 Flash models, it achieved record-breaking self-correction performance, improving the baseline models by 15.6% and 9.1%, respectively, in MATH and HumanEval tests.

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Over the past month, there have been some very interesting and significant events in the world of Large Language Models (LLM).

Major companies have released fresh versions of their models. First, Google launched two new models, Gemini: Gemini-1.5-Pro-002 and Gemini-1.5-Flash-002.

Key Features:

• More than a 50% price reduction for the 1.5 Pro version
• Results are delivered twice as fast with three times lower latency

The main focus has been on improving performance and speed and reducing costs for models intended for industrial-grade systems.

Details here

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