Explore foundation models and capabilities in Azure AI Foundry
🧩 Problem Statement
Building generative AI applications requires: - Selecting the right foundation model from thousands available - Understanding model capabilities and limitations - Deploying models efficiently for production use - Optimizing model performance through prompt engineering - Scaling solutions for real-world workloads
Key Challenges:
- How to choose between LLMs vs SLMs?
- When to use proprietary vs open-source models?
- How to evaluate model precision and performance?
- How to optimize outputs through prompt engineering?
- How to deploy and scale models effectively?
💡 Solution with Azure
Azure AI Foundry provides a comprehensive model catalog with tools for exploration, deployment, and optimization of foundation models. The platform supports both proprietary models (GPT-4, Mistral) and open-source alternatives from Hugging Face, enabling developers to build scalable generative AI applications.
🧩 Required Components
Foundation Model Types
Language Models - Generate, understand, and interact with natural language - Common use cases: speech-to-text, translation, text classification, entity extraction, summarization, Q&A, reasoning
Model Categories - Large Language Models (LLMs): GPT-4, Mistral Large, Llama3 70B - for deep reasoning and complex content - Small Language Models (SLMs): Phi3, Mistral OSS, Llama3 8B - efficient and cost-effective - Chat Completion Models: GPT-4, Mistral Large - generate contextual text responses - Reasoning Models: DeepSeek-R1, o1 - enhanced performance for math, coding, science - Multi-Modal Models: GPT-4o, Phi3-vision - process text and images - Image Generation: DALL-E 3, Stability AI - create visuals from text - Embedding Models: Ada, Cohere - convert text to numerical representations - Regional/Domain-Specific: Core42 JAIS (Arabic), NorlaTimeGEN-1 (time series)
Model Catalogs
- Hugging Face: Vast catalog of open-source models
- GitHub: Access via GitHub Marketplace and Copilot
- Azure AI Foundry: Comprehensive catalog with deployment tools
🛠 Architecture & Development
🔹 Model Selection Framework
Step 1: Can AI solve my use case? - Explore available models through three catalogs - Filter and deploy models based on needs - Test with different model types
Step 2: How do I select the best model?
Evaluation Criteria: - Task Type: Text only vs multi-modal requirements - Precision: Base model vs fine-tuned for specific skills - Openness: Fine-tuning capability requirements - Deployment: Local vs serverless endpoint needs
Step 3: Can I scale for real-world workloads?
Scaling Considerations: - Model deployment strategy - Model monitoring and optimization - Prompt management - Model lifecycle (GenAIOps)
🔹 Performance Evaluation
Model Benchmarks: | Benchmark | Description | |-----------|-------------| | Accuracy | Compares generated text with correct answers (1 if exact match, 0 otherwise) | | Coherence | Measures if output flows smoothly and resembles human language | | Fluency | Assesses grammatical rules, syntax, and vocabulary usage | | Groundedness | Measures alignment between generated answers and input data | | GPT Similarity | Semantic similarity between ground truth and AI predictions | | Quality Index | Aggregate score 0-1, higher is better | | Cost | Price per token for model usage |
Evaluation Methods: - Manual evaluations for initial quality assessment - Automated evaluations using metrics (precision, recall, F1 score) - Traditional ML metrics + AI-assisted metrics
🔹 Model Deployment
Deployment Process: 1. Select model from catalog 2. Deploy to endpoint (creates unique URI and key) 3. Send API requests to endpoint 4. Receive and process responses
Deployment Options:
| Service | Supported Models | Hosting | Cost Model | Inferencing |
|---|---|---|---|---|
| Azure OpenAI Service | Azure OpenAI models | Azure OpenAI resource | - | Token-based billing |
| Azure AI Foundry Models | Flagship models (OpenAI + MaaS) | Azure AI Services resource | - | Token-based billing |
| Serverless compute | MaaS service models | AI Project resource | Minimal endpoint cost | Token-based billing |
| Managed compute | Open and custom models | AI Project resource | Charged per minute | - |
🔹 Prompt Engineering Optimization
Core Patterns: 1. Persona Instructions: "Act as a seasoned marketing professional" 2. Better Questions: Guide model to suggest clarifying questions 3. Format Specification: Provide templates for output structure 4. Reasoning Explanation: Use chain-of-thought for step-by-step logic 5. Context Addition: Provide relevant background information
Advanced Optimization Strategies:
Context Optimization (What model needs to know)
↓
- Retrieval Augmented Generation (RAG): Grounding with data sources
- Prompt Engineering: Optimize through patterns
- Combined Strategies: Mix approaches
- Fine-tuning: Extend training with examples
→ Model Optimization (How model needs to act)
🆚 Model Comparison Framework
| Aspect | LLMs | SLMs |
|---|---|---|
| Use Cases | Deep reasoning, complex content | Common NLP tasks |
| Resources | High compute requirements | Edge device compatible |
| Cost | Higher operational cost | Cost-effective |
| Speed | Slower processing | Faster response times |
| Examples | GPT-4, Mistral Large | Phi3, Mistral OSS |
| Aspect | Proprietary | Open-Source |
|---|---|---|
| Performance | Cutting-edge, enterprise support | Flexible, customizable |
| Control | Limited customization | Full control, fine-tuning |
| Security | Built-in enterprise features | Self-managed |
| Cost | Higher licensing | Cost-effective |
🧠 Best Practices & Considerations
Transformer Architecture Foundation
- Parallel Processing: Words processed independently using attention mechanism
- Positional Encoding: Maintains word position information in sentences
- Based on "Attention is all you need" paper (Vaswani et al., 2017)
Model Selection Best Practices
- Start with task requirements (text-only vs multi-modal)
- Consider precision needs (base vs fine-tuned)
- Evaluate deployment constraints (local vs cloud)
- Test with benchmarks before production
- Plan for scaling from prototype to production
Prompt Engineering Guidelines
- Use system prompts to set model behavior
- Apply one-shot/few-shot examples for pattern recognition
- Request specific output formats with templates
- Implement chain-of-thought for complex reasoning
- Add grounding context for accuracy (RAG pattern)
Performance Optimization
- Start with prompt engineering (lowest cost/complexity)
- Consider RAG for grounding responses with data
- Use fine-tuning only when necessary
- Combine strategies for optimal results
- Monitor and iterate based on metrics
🎯 Exam Simulation Questions
Q: Which models are best for tasks requiring deep reasoning and extensive context understanding? ✅ Large Language Models (LLMs) like GPT-4, Mistral Large, Llama3 70B
Q: What technique involves using a data source to provide grounding context to prompts? ✅ Retrieval Augmented Generation (RAG)
Q: Which benchmark measures whether model output flows smoothly and resembles human language? ✅ Coherence
Q: What are the four key criteria for selecting the best language model? ✅ Task type, Precision, Openness, Deployment
Q: Which deployment option is best for open-source models with custom requirements? ✅ Managed compute
Q: What prompt engineering pattern helps models provide step-by-step explanations? ✅ Chain-of-thought (asking for reasoning explanation)
Q: Which models are ideal for edge devices with limited resources? ✅ Small Language Models (SLMs) like Phi3, Mistral OSS
Q: What is the unique identifier format when deploying a model to Azure AI Foundry? ✅ https://ai-[hubname].openai.azure.com/openai/deployments/[model-name]/chat/completions?api-version=[version]