OpenAI and ChatGPT repo

Theoretical Part. Table of Content

0. Six Principles of responsible AI
1. Responsible AI. Trusted AI Framework. Content Filters. Harmful Content. Prerelease Reviews
2. What is ChatGPT Doing. and why does it work
3. LLM UseCase in Google. Sorting Optimization
4. Embeddings. Words to Vector. Useful in Search Scenarios and for Cognitive Search
5. Cognitive Search. Video
6. Cognitive Search. From Zero to Hero
7. Cognitive Search. Indexers. AI Enrichment. Build-in Skills
8. Transformers. Embeddings. Foundational Model
9. Computer Vision. Cognitive. AI Face. Custom Vision
10. Document Intelligence
11. Azure AI Speech. Speech To Text. Text To Speech. Azure Services
12. Natural Language Processing(NLP). Text Meaning and analysis. General ways how to
13. Azure Language Service. Commands interpretation
14. Azure Language Service. Question-Answer Knowledge base for bots. Question Answering service.
15. Regression. Logistic and Linear Regression. Multiclass regression

Azure Learn Useful Materials

1. Composed Document Intelligense Models. Case if you need to analyze several doc types
2. Vision. Train a Custom Model using COCO
3. Deploy AI Services in Containers, in AKS, ACI, or even locally
4. Analyze Video Indexer. Widgets Integration and API
5. Semantic Ranking configuration in AI Search Index
6. Knowledge Store & Knowledge Mining with AI Search

Machine Learning Materials

1. Machine Learning
   a. Machine Learning lab by Microsoft
2. How Deep Learning Works

My LinkedIn Posts & Presentations

1. GenAI. Where could be applied. Post 1.pdf
2. GenAI in Application Refactoring field, Slides.pdf
3. Legal problems with AI.pdf
4. Paradigms: Rag, Self-RAG, Re-Ranking RAG, FLARE v.2.pdf
5. Working with opinionated requests. S2A, RLHF, RLAIF.pdf
6. Multi-Modal RAG and its features.pdf
7. Measuring the GenAI Quality.pdf
8. LLM leveraging RLHF in code review
9. Everything of Thoughts (XoT). All modern techniques in one place
10. Non deterministic embedding results
11. AI Search vs PostgreSQL with pgvector in PROD
12. Prod-Ready LLM Solutions. Cook Book.
13. Crew.AI. Agents in LLM Applications (In Progress)
14. Pydantic data classes and how to manage the output format (In Progress)
15. XML vs Markdown vs Json for tagging in prompting and metaprompting (In Progress)
16. Crawlers for LLMs: https://python.langchain.com/v0.1/docs/use_cases/web_scraping/ , https://ai.gopubby.com/use-ai-to-scrape-almost-all-websites-easily-in-2025-f868adc41e0f, https://github.com/Skyvern-AI/skyvern, https://gotenberg.dev/docs/routes, https://jina.ai/reader, https://github.com/unclecode/crawl4ai, https://crawlee.dev/, https://github.com/bracesproul/site-rag/, https://www.firecrawl.dev, https://github.com/mishushakov/llm-scraper
17. Table extraction in RAG systems (In Progress)

My Workshops

1. June 2023. My Workshop Presentation. Run 1.pptx
2. Online Workshop. ChatGPT -> Azure Function -> PowerAutomate. Run 2.pptx
3. Online Workshop. Run 3. Deep Learning -> Prompting -> ChatGPT -> Azure Function -> PowerAutomate
4. Online+Offline Workshop for EHU University
5. Talk #3. RAG, FLARE, S2A, RLHF, RLAIF, Self-RAG, Re-Ranking. Common approaches and their pros & cons

Extra materials

1. Vector Database selection & comparison. VectorDB
2. Transformer Explainer. Transformer Explainer is an interactive visualization tool designed to help anyone learn how Transformer-based models like GPT work
3. Table extraction in RAG systems

Practical Part. Table of Content

1. Example:ConsoleApp CommandGuess
2. Example: Azure Function with ChatGPT (completion and chat-completion)
3. Example: Integration with PowerAutomate
4. Example: Integration with PowerApp
5. Integration with Outlook (In progress)
6. OpenAI + PowerAutomate Workshop by me.pptx
7. Example: OpenAI + Redis
8. BMW Dealer assistant. ChatGPT Chat + Startup + Redis + Context
9. Get Embedding
10. Form Recognizer Cognitive Service
11. Content Filters (in progress)
12. OpenAI straightforward examples
13. Azure Bot Service & Chatbot Framework
14. LangChain meets Go
15. TenzorZero Framework (In progress)

Semantic Kernel

Semantic Kernel. Knowledge base

1. Semantic kernel and AI Assistant
2. Creative Writing Assistant with Semantic Kernel and .Net Aspire

SemanticKernel. Practical part

1. Initial Example
2. Interactive Chat with Chat History
3. Model Switching. Hugging Face
4. Semantic Function for Conversational Chat
5. Semantic Kernel Pipeline

LangChain

LangChain using Golang

1. General Examples (In Progress)

Azure

Azure Search & Document Intelligence. Theoretical Part

1. Cognitive Search. Video
2. Cognitive Search. From Zero to Hero
3. Cognitive Search. Indexers. AI Enrichment. Build-in Skills
4. Document Intelligence

Azure Cognitive Search & Document Intelligence. Practical Part

1. Semantic Search (in progress)
2. Document Intelligence (in progress)
3. Semantic Search vs Document Intelligence (in progress)

General Information

0: RAG. Cheatsheet

1: PowerAutomate. React on manual trigger

2: PowerAutomate. React on keyword mentioned

.Net OpenAI SDK

.Net SDK (unofficial): https://github.com/Glareone/openai

OpenAI integration ideas

ChatGPT. GPT3.5 vs GPT4

LLM Orchestration. LangChain & Semantic Kernel

Lang Chain