The Secret Life of Azure: The Voice That Read Everything (RAG)
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The Secret Life of Azure: The Voice That Read Everything (RAG)
Building private, grounded AI assistants with Azure OpenAI and RAG
#Azure #OpenAI #AI #DevOps #GenerativeAI
🎧 Audio Edition: Prefer to listen? Check out the expanded AI podcast version of this deep dive on YouTube.
📺 Video Edition: Prefer to watch? Check out the 7-minute visual explainer on YouTube.
Data & AI
The library was glowing with the light of the "Smart Index," but Timothy was still watching patrons struggle to synthesize information from twenty different scrolls at once.
"Margaret," Timothy said, "the AI Search is brilliant—it finds the right books instantly. But the patrons are overwhelmed. They don't want a list of ten books about 'how to build a bridge'; they just want to know how to build the bridge. They want to talk to someone who has already read every book in the basement and can summarize the answer for them. But I can't be everywhere at once."
Margaret walked to the center of the room and gestured to the air itself. "Timothy, you've given the library a memory and a map of meaning. Now, it’s time to give it a Voice. We are going to introduce Azure OpenAI Service."
She drew a speech bubble on the chalkboard and connected it to the "Smart Index" magnifying glass.
The Intelligent Speaker: Large Language Models
"Azure OpenAI," Margaret explained, "is like a master linguist. It has studied billions of sentences and understands how to communicate. But on its own, it doesn't know what is inside our library. It’s like a genius who has never visited our building."
[Image showing a Large Language Model (LLM) as a reasoning engine]
Timothy looked puzzled. "If it doesn't know our books, how can it help our patrons?"
The Secret Sauce: RAG (Retrieval-Augmented Generation)
Margaret drew a line from the "Smart Index" to the "Voice." "We use a pattern called RAG. When a patron asks a question, we don't just hand it to the AI. First, our AI Search retrieves the most relevant snippets from our basement. Then, we hand those specific snippets to the AI and say: 'Using only these facts, answer the patron’s question.'"
Timothy’s eyes widened. "So the AI isn't just guessing based on what it learned in school? It’s literally 'reading' the books we give it in real-time to formulate the answer?"
"Exactly," Margaret replied. "We call this Groundedness. It prevents the AI from 'hallucinating' or making things up. It can only speak from the truth of our shelves."
Security: The Private Conversation
"And our rare scrolls?" Timothy asked. "Does the AI take our secrets back to its own school to teach others?"
"Never," Margaret said firmly. "With Azure OpenAI, your data stays in your library. It is never used to train the global models. Your 'Voice' is private, secure, and stays within your boundaries."
Putting It into Practice
Timothy walked up to the glowing desk and asked: "How do I preserve a 15th-century map?"
Within seconds, the Voice replied: "According to the scrolls in our Archive Tier, you must maintain a humidity of 50% and use acid-free vellum..."
"It’s not just searching," Timothy whispered. "It’s teaching."
Key Concepts
- Azure OpenAI Service: Provides REST API access to OpenAI's powerful language models including the GPT-4 and GPT-3.5 series.
- Large Language Model (LLM): An AI trained on vast amounts of text that can understand and generate human-like language.
- RAG (Retrieval-Augmented Generation): An architecture that provides an LLM with specific, retrieved data to ensure answers are accurate and up-to-date.
- Groundedness: The practice of ensuring AI responses are based on provided, factual source material.
- Hallucination: When an AI generates factually incorrect or nonsensical information. RAG is the primary "cure" for this.
- Data Privacy: Azure OpenAI ensures that customer data is not used to train the underlying foundation models.
Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.
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