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AI-driven Software Development with Azure AI

AI-driven Software Development with Azure AI

Jörg Neumann

August 27, 2024
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  1. TOPICS  AI  Frontend Technologies  Mobile Development 

    Consulting, Coaching, Training CONTACT  Mail: [email protected]  Twitter: @JoergNeumann  Web: www.neogeeks.de JÖRG NEUMANN Founder & CEO NeoGeeks GmbH Azure OpenAI Service, Azure Machine Learning Platform
  2. RETRIEVAL AUGMENTED GENERATION (RAG) GOAL  Extend the model with

    external data sources  Integrate domain-specific knowledge  Model doesn’t need to be trained
  3. RETRIEVAL ARGUMENTED GENERATION (RAG) Chunk Chunk Chunk Chunk Embeddings Embeddings

    Embeddings Embeddings Vector Database PDF Document user search Embeddings Search answer LLM weight results
  4. PROMPT ENGINEERING Answer the question based on the context provided

    below. If the question cannot be answered with the provided information, respond with 'I don't know’. context: Large Language Models (LLMs) are the latest models used in NLP. Their superior performance over smaller models has made them incredibly useful for developers. These models can be accessed via the 'transformers' library by Hugging Face, via OpenAI with the 'openai' library, and via Cohere with the 'cohere' library. question: Which libraries and model providers offer LLMs?“ instructions prompt external information Which libraries and model providers offer LLMs?
  5. IMPLEMENTATION CHALLENGES  Tables and illustrations in documents  Answers

    have no source citations  Answers can be outdated  The model can hallucinate  Limit context to specific data  Content filters
  6. IMPLEMENTATION CODING  Python, C#, Java FRAMEWORKS  LangChain, LlamaIndex,

    …  Semantic Kernel  Spring AI VECTOR STORES  Pinecone, Chroma, Milvus, Qdrant, …
  7. AI IN AZURE AZURE OPENAI SERVICE  Models from OpenAI

     Endpoint for API access  Available models depend on region AZURE OPENAI STUDIO  Deployment and configuration of the models
  8. AI IN AZURE AZURE AI STUDIO  Development of complex

    AI solutions  Multi-Model Support  Prompt Flow  Debugging & Metrics  https://ai.azure.com
  9. CONCLUSION  RAG is a complex pattern  Numerous frameworks

    and tools are required  Azure OpenAI and Azure AI Search offers an easy low-code solution for RAG scenarios  Complex scenarios can be implemented with Azure AI Studio