Shchegrikovich’s Newsletter

Shchegrikovich’s Newsletter focuses on the intricacies of Large Language Models (LLMs), Generative AI (GenAI), and their applications, exploring models' architecture, development stages, comparisons, and enhancement techniques. It covers topics such as fine-tuning, multimodal capabilities, and security strategies, emphasizing efficiency, scalability, and minimizing inaccuracies in AI applications.

Large Language Models Generative AI Applications Model Architecture and Development Fine-tuning Techniques AI Security and Threat Modeling AI and Knowledge Management Platforms Multimodal LLM Capabilities AI Efficiency and Scalability Prompt Engineering AI Agent Architecture

The hottest Substack posts of Shchegrikovich’s Newsletter

And their main takeaways

Mixture of Experts. The magic behind Mixtral models

19 implied HN points • 04 Feb 24
  1. Mixture of Experts (MoE) architecture consists of routing and experts, unlike Transformers architecture.
  2. MoE is a sparsely activated network, while Transformers is a dense network, which allows for efficient scaling of models.
  3. Experts in a MoE model cluster tokens based on similar token-level semantics, showing context-independent specialization.

4 stages of LLM app value creation and development.

2 HN points • 06 Nov 23
  1. Stage 1: Start with a simple prompt to test user value but be wary of it being easy to replicate by competitors.
  2. Stage 2: Advance to using complex prompt techniques like Chain of Thought and Step-back prompting for multi-step execution.
  3. Stage 3: Enhance the app with a Knowledge Management Platform by adding RAG, memory, and a Knowledge Base for high-level value and a barrier to entry for competitors.

How do AI Agents think?

1 HN point • 24 Dec 23
  1. First strategy: AI Agents can communicate through text responses.
  2. Second strategy: The app works by taking action on a prompt given, like setting an alarm.
  3. Third strategy: Using a swarm of agents, each with different roles, can improve efficiency.

Fine-tuning of LLMs

0 implied HN points • 28 Sep 23
  1. GPT-4 may not be the only necessary model, as specialized models for different domains are becoming more common.
  2. Fine-tuning can improve model quality for specific tasks by providing new data and properly defined evaluation metrics.
  3. Before fine-tuning, consider options like prompt refinement, few-shot prompting, example selection, and retrieval assisted generation.
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Attacks against LLM-based apps and how to prevent them

0 implied HN points • 27 Dec 23
  1. Attacks against LLM-based apps require unique threat modeling and prevention strategies.
  2. Set up trust boundaries in applications when using LLMs to prevent security vulnerabilities.
  3. Utilize protection techniques like red teaming, tools such as Llm-guard, and resources like AI Incident Database to enhance security.

Multimodal capabilities of LLMs

0 implied HN points • 04 Dec 23
  1. Multimodal capabilities of LLMs involve understanding and generating content in various forms beyond text.
  2. Models like Fuyu-8B and LLaVA introduce features like visual question answering, image captioning, and more.
  3. Two approaches to adding images to LLMs include specific visual encoders or connecting image patches directly to transformer layers.

Zoo of RAGs

0 implied HN points • 11 Feb 24
🕹 Technology AI Machine Learning Information Retrieval Natural Language Processing
  1. Retrieval Augmented Generation (RAG) improves LLM-based apps by providing accurate, up-to-date information through external documents and embeddings.
  2. RAPTOR enhances RAG by creating clusters from document chunks and generating text summaries, ultimately outperforming current methods.
  3. HiQA introduces a new RAG perspective with its Hierarchical Contextual Augmentation approach, utilizing Markdown formatting, metadata enrichment, and Multi-Route Retrieval for document grounding.

Embeddings

0 implied HN points • 22 Sep 23
  1. Embeddings are an ML model that converts text to vectors for various applications like semantic search and content moderation.
  2. There are different embedding models available, both free and paid, that can provide instant improvements over traditional approaches.
  3. Utilizing vector databases and trained embedding models can enhance tasks like converting the entire internet into an embedding database.

Hallucinations

0 implied HN points • 22 Sep 23
  1. LLMs can generate inaccurate information, known as hallucinations, creating problems for app builders and enterprise adoption.
  2. One way to prevent hallucinations is by providing better context through techniques like RAG (Retrieval-augmented generation).
  3. An alternative approach for LLM architecture involves using modular AI systems with specialized sub-systems for various tasks.

Anatomy of AI agents.

0 implied HN points • 20 Nov 23
  1. AI agents use artificial intelligence to achieve specific goals by breaking them down into actionable tasks.
  2. AI agents consist of Observation Receiver, Memory, Planner, and Action Executor components connected to an Environment.
  3. The power of AI agents lies in their ability to communicate and collaborate with each other to accomplish common goals.

How does RAG improve LLM-based applications?

0 implied HN points • 14 Oct 23
  1. RAG improves LLM-based applications by providing up-to-date information, reducing hallucinations, and enabling access to private data.
  2. Implementing RAG involves splitting documents into chunks, generating embeddings, saving them in a vector store, retrieving relevant content, and synthesizing it in response to user requests.
  3. RAG is not limited to text documents but can also be used for querying APIs and offers various implementations like in Python LangChain and C# SemanticKernel.

How to improve the quality of prompts for LLMs? Why does a prompt like "take a deep breath…" improve the performance of LLM?

0 implied HN points • 27 Nov 23
  1. Crafting prompts is crucial to guide Large Language Models to specific regions in Latent Space.
  2. Experimentation and operation are essential steps beyond crafting prompts to enhance user experience.
  3. Sparse Priming Representation (SPR) can help achieve desired results with shorter prompts, reducing pressure on the context window of Large Language Models.

What is actually sent to the LLM?

0 implied HN points • 21 Jan 24
  1. Before using a language model, you must understand the input it expects.
  2. Prompt design and instruction format are crucial for the model's performance.
  3. The format of the input can impact the model's accuracy significantly.

The power of small LLMs.

0 implied HN points • 13 Jan 24
  1. Phi2 from Microsoft is a popular small language model with 220K downloads on HuggingFace.
  2. Phi2 is based on the idea of reducing training dataset size but increasing quality, resulting in excellent performance.
  3. To fine-tune a language model like Phi2 for specific tasks, datasets like the OpenAssistant Conversations Dataset can be valuable.

Architecture of GenAI app

0 implied HN points • 23 Oct 23
  1. Development of a GenAI app starts with experiments and prototypes before moving to a high-level understanding of the app
  2. Components like Knowledge Base, RAG, and Reasoning Engine play crucial roles in the GenAI app architecture
  3. Fine-tuning and improving components like Training Pipeline, Model Registry, and DataSet Generator are essential for application quality

How to compare LLama2 to ChatGPT?

0 implied HN points • 13 Nov 23
  1. Evaluation of Large Language Models involves testing in categories like Knowledge and Capabilities, Alignment, and Safety.
  2. Specialized LLMs are evaluated based on specific benchmarks aligned with their focus, such as medical exams for Medical LLMs.
  3. Transparency in models is crucial for evaluation, and different approaches like Red Teaming and Model-based evaluation are important to address biases and uncertainties.

GenAI-trification

0 implied HN points • 20 Oct 23
  1. GenAI-trification is a significant trend approaching, with businesses like Shopify and HubSpot integrating GenAI into their strategies.
  2. Creating content and assisting users are the core approaches in incorporating GenAI into product management.
  3. Senior executives lead 70% of digital transformations, and 60% of organizations have been using AI in marketing for less than a year.