Sunday, October 20, 2024

GenAI and LLMs development, trends and implications (7. - 13.10.2024)

Adoption:
LLMs generally:
New models and functionality:
AI agents:
AI-enhanced software development:
Enhancing applications with GenAI:

Saturday, October 19, 2024

IT links (7. - 13.10.2024)


       Java Streams:




Sunday, October 13, 2024

GenAI and LLMs development, trends and implications (30.9. - 6.10.2024)

Adoption:
LLMs generally:
AI agents:
RAG:
AI-enhanced software development:
Enhancing applications with GenAI:

Monday, April 15, 2024

Začínám psát články v češtině na Seznam Médium

Nedávno jsem se rozhodl začít psát články v češtině na platformě Seznam Médium. Snažím se zaměřit hlavně na vědu a technologie, ale samozřejmě záběr může být o dost větší. Zde je seznam do teď mnou napsaných článků:

Proč se nedožíváme 200 let? Možná za to mohou dinosauři

Rok 2023 v letech do vesmíru

Multivitamíny – klíč k zachování kognitivního zdraví ve stáří?

Vorvani žijí v klanech podobných těm, v jakých žili první lidé

T-lymfocyty jako pramen mládí?

Strojové učení pomáhá překonat jeden z největších problémů jaderné fúze

Aktuální seznam mnou publikovaných článků můžete nalézt na mém profilu zde: Primus Immortails

Saturday, April 13, 2024

Exploring Advanced AI Techniques: Ghost Attention, Thought Structures, Prompt Engineering and more

Diving deeper into the realm of generative AI, I've come across several articles that I find interesting as a beginner in this field:

The article - Understanding Ghost Attention in LLaMa 2 - delves deep into the technique of the ghost attention technique in LLaMa 2.

One example of providing instructions for specific chat is Prompt Instructions in Watsonx IBM service:

You define instructions in the upper input and then start to chat below.

One detailed look into how generative AI works is this article exploring the differences between "Chain of thoughts" and "Tree of thoughts" - Chain of Thoughts vs Tree of Thoughts for Language Learning Models (LLMs)

How to work better with these systems? You can improve the output using prompt patterns or n-shot prompting - 7 Prompt Patterns You Should Know

For controlling grounding data used by an LLM and constraining it for your enterprise Gen AI solutions, consider using Retrieval Augmented Generation (RAG). You can see how to use it, for example in Azure, here - Retrieval Augmented Generation (RAG) in Azure AI Search

Additionally, to gain more from LLMs, you can explore architecture patterns and mental models as described here - Generative AI Design Patterns: A Comprehensive Guide

Saturday, December 2, 2023

A comprehensive overview of generative AI and LLMs' trends, use cases, and future implications II. - Engineering and development insights

7 weeks (from 4.9. to 22.10.2023) in the world of Large Language Models and Generative AI tools, this time more focused on the engineering side:


Prompt engineering:

Parallel processing in prompt engineering: the skeleton-of-thought technique.

Unlocking reliable generations through Chain-of-Verification - a leap in prompt engineering.

LLMOps: production prompt engineering patterns with Hamilton.

Crafting different types of program simulation prompts - defining the new program simulation prompt framework.

Some kick-ass prompt engineering techniques to boost our LLM models.

And other prompt engineering tips, a neural network how-to, and recent must-reads.


AI Development and Engineering:

The team behind GitHub Copilot shares its lessons from building the app.

Amazon Bedrock for building and scaling generative applications is now generally available.

Experience from building generative AI apps on Amazon Web Services, using Amazon Bedrock and SageMaker.

A guide with 7 steps for mastering LLMs.

Key tools for enhancing Generative AI in Data Lake Houses.

An introduction to loading Large Language models.

Introduction to ML engineering and LLMOps with OpenAI and LangChain.

MLOps and LLM deployment strategies for software engineers.

Modern MLOps platform for Generative AI.

Leveraging the power of LLMs to guide AutoML hyperparameter searches.

LLMs demand Observability-Driven Development.

LLM monitoring and observability — a summary of techniques and approaches.

How to build and benchmark your LLM evals.

A step-by-step guide to selecting and running your own generative model.

Google Research: Outperforming larger language models with less training data and smaller model sizes - distilling step-by-step.

Google Research: Rethinking calibration for in-context learning and prompt engineering.

Apache Kafka as a mission-critical Data Fabric for GenAI.

Training ChatGPT on your own data.

Hugging Face's guide to optimizing LLMs in production.

Hugging Face is becoming the "GitHub" for Large Language Models.

Building microservice for multi-chat backends using Llama and ChatGPT.

Connect GPT models with company data in Microsoft Azure.

Tuning LLMs with MakerSuite.

Fine-tuning LLMs: Parameter Efficient Fine Tuning (PEFT), LoRA and QLoRA.

How to train BERT for masked language modeling tasks.

Extending context length in Large Language Models.

Conversational applications with Large Language Models understanding the sequence of user inputs, prompts, and responses.

Using data lakes and Large Language Models in development.

How to build an LLM from scratch.

LLM output parsing: function calling vs. LangChain.

Enhancing the power of Llama 2: 3 easy methods for improving your Large Language Model.


Keeping LLMs relevant and current - Retrieval Augmented Generation (RAG).

Build and deploy Retrieval Augmented Generative Pipelines with Haystack.

Why your RAG is not reliable in a production environment.


QCon San Francisco: 

Unlocking enterprise value with Large Language Models.

A modern compute stack for scaling large AI, ML, & LLM workloads.