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Building a Custom GPT for Your Documentation with Azure OpenAI
This pattern works for any documentation. The key is good chunking, quality embeddings, and a well-crafted system prompt that keeps the bot focused on your…
Azure OpenAI
71 articles
1 min read
Building Multi-Agent Systems with AutoGen and Azure
Instead of a single LLM handling all tasks, multi-agent systems divide work among specialized agents that communicate and coordinate to achieve goals.
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Model Fine-Tuning on Azure: When and How to Customize LLMs
Fine-tuning makes sense when you need consistent style, specialized terminology, or improved performance on specific tasks that prompt engineering cannot…
1 min read
Semantic Kernel: Orchestrating AI Agents with Plugins and Planners
Semantic Kernel abstracts the complexity of working with multiple AI services while providing extensibility through plugins. This allows developers to…
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Prompt Engineering Patterns for Enterprise RAG Systems
Poorly designed prompts lead to hallucinations, ignored context, or responses that fail to meet business requirements. A systematic approach to prompt…
1 min read
Batch Processing AI Workloads with Azure Batch and OpenAI
Track job progress and handle failures with proper retry logic for robust batch AI processing at scale.
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GraphRAG: Combining Knowledge Graphs with Retrieval-Augmented Generation
GraphRAG excels when your questions involve multi-hop reasoning, relationship discovery, or when entity connections matter more than document similarity.
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Building Copilot Extensions with Azure OpenAI Assistants API
The Assistants API provides the foundation for building powerful, context-aware AI assistants that integrate with enterprise systems.
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Cost Optimization Strategies for Azure OpenAI Deployments
Balance cost and quality by routing requests to appropriate models based on task requirements.
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Building AI Agents with AutoGen and Azure OpenAI
AutoGen enables building sophisticated AI systems that can decompose complex tasks and collaborate to find solutions.
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Azure Functions with AI: Building Serverless AI Applications
Configure appropriate timeout settings for AI operations and implement retry logic for transient failures. Consider using premium plans for…
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Implementing RAG with LangChain and Azure OpenAI
Experiment with chunk sizes, overlap, and retrieval strategies. Hybrid search combining semantic and keyword matching often provides the best results for…
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Semantic Kernel: Building AI Orchestration with .NET
Semantic Kernel provides a clean abstraction for building AI-powered features while maintaining flexibility to switch between different LLM providers.
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Fine-Tuning Azure OpenAI Models: When and How to Customize
Fine-tuning is appropriate when you need consistent formatting, domain-specific language understanding, or reduced prompt lengths. However, it requires…
1 min read
Prompt Engineering Patterns for Enterprise Applications
Include examples in prompts to guide model behavior for domain-specific tasks. This is especially effective for classification and formatting tasks where…
1 min read
Building Multi-Modal AI Applications with GPT-4 Vision and Audio
Multi-modal requests consume more tokens and have higher latency. Use the detail parameter wisely: "low" for quick analysis, "high" for detailed extraction…
1 min read
AI Safety Guardrails: Implementing Content Filtering in Azure OpenAI
Maintain comprehensive logs of all content filtering decisions for compliance and incident investigation. Every blocked request should be logged with…
1 min read
Fine-Tuning GPT Models: When and How to Customize
Fine-tuning makes sense when you need consistent formatting, domain-specific terminology, or significant behavior changes that prompting cannot achieve…
1 min read
Structured Output with Azure OpenAI: JSON Mode and Response Formats
JSON mode guarantees the model outputs valid JSON, though you still need to specify the schema in your prompt.
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Function Calling Patterns in Azure OpenAI
Functions are defined as JSON schemas that describe parameters and their types. Well-designed function schemas and clear descriptions ensure the model calls…
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GPT-4o Vision: Building Image Analysis Applications
GPT-4o vision provides remarkable understanding of images, but always validate outputs for critical applications. Combine with traditional computer vision…
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Cost Optimization Strategies for Azure OpenAI Workloads
Semantic caching avoids redundant API calls for similar queries. Reduce prompt length by removing unnecessary context. Use batch processing with the Batch…
1 min read
Azure OpenAI o1 Models: Reasoning at Scale for Complex Problems
The o1 models are designed for tasks requiring deep reasoning: mathematical proofs, code debugging, scientific analysis, and strategic planning. They take…
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Building RAG Applications with Azure AI Search and GPT-4o
RAG works by first retrieving relevant documents from a search index, then passing those documents as context to an LLM for generation. This approach…
1 min read
Building Knowledge Graphs with Azure OpenAI and Neo4j
Graph RAG significantly improves answers for questions involving relationships, hierarchies, and multi-hop reasoning.
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Fine-Tuning GPT-4o for Domain-Specific Tasks on Azure
After training completes, evaluate on a held-out test set before deploying. Monitor the fine-tuned model's performance against the base model to ensure…
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Prompt Caching with Claude and Azure OpenAI: Reducing Latency and Costs
Track cache hit rates in your monitoring. Applications with high context reuse typically see 60-80% cache hit rates, dramatically reducing per-request costs.
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GPT-4o Vision for Document Intelligence: Building Smart OCR Pipelines
For multi-page documents, process pages in parallel and use a synthesis step to merge extracted data. GPT-4o handles cross-page references like "continued…
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Azure AI Updates: What's New in February 2025
Stay current with Azure AI updates to leverage the latest capabilities for your data and AI applications.
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Azure OpenAI November 2024 Updates: What's New for Enterprise AI
Batch processing can reduce costs by up to 50% for workloads that don't need immediate responses.
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Tool Use Patterns for AI Agents
Tools extend what AI agents can do beyond text generation. Today I'm exploring patterns for effective tool use in production agents.
3 min read
GPT-4o: Multimodal AI Gets Real-Time
Total latency: 2-5 seconds. GPT-4o processes audio natively - 232ms average response time. That's human conversational speed. The model understands tone…
1 min read
Deploying GPT-4o on Azure OpenAI Service
1. Navigate to your Azure OpenAI resource 2. Go to Model deployments Deploy model 3. Select from the model list 4. Configure deployment settings 5. Deploy…
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The Future of Multimodal AI: What's Next After GPT-4 Vision
Each modality is handled separately, then combined. This works, but has latency and integration challenges.
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DALL-E 3 Patterns: Effective Image Generation for Enterprise
I experimented with DALL‑E 3 for enterprise image generation; these patterns capture what scaled well and what to avoid.
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GPT-4 Vision Patterns: Building Visual AI Applications
I've used GPT-4 Vision in real projects; these are the practical patterns that helped move visual AI from experiment to production.
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Provisioned Throughput Deep Dive: Architecture and Implementation
1. Multi region deployment Distribute for resilience 2. Monitor continuously Track utilization and latency 3. Plan scaling windows PTU changes aren't…
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Azure OpenAI PTU: Provisioned Throughput Units Explained
Aspect Pay As You Go PTU Pricing Per token Per PTU hour Throughput Shared, variable Dedicated, consistent Latency Variable Lower, consistent Commitment None…
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Prompt Caching: Reducing Latency and Cost for Repetitive Contexts
Many applications send the same system prompt and context repeatedly: 1. Identify common prefixes System prompts, few shot examples 2. Batch similar…
1 min read
Token Cost Analysis: Understanding and Managing LLM Expenses
Token cost analysis enables informed decisions about LLM usage. Track, analyze, and optimize to keep costs predictable and manageable.
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Cost Optimization for LLMs: Strategies That Work
1. Cache aggressively Identical queries don't need re processing 2. Tier your models Match model to task complexity 3. Optimize prompts Shorter prompts =…
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Multi-LLM Strategies: Building Resilient AI Applications
Resilience : Failover when one provider has outages Cost optimization : Route to cheaper models when possible Capability matching : Use best model for each…
1 min read
Gemini vs GPT: Practical Comparison for Enterprise Applications
I spent time comparing Gemini and GPT in enterprise settings; below are the practical differences that should influence model choice.
1 min read
Integrated Vectorization in Azure AI Search: Simplifying RAG Pipelines
Integrated vectorization removed an entire pipeline step in a recent project. I'll show how it simplifies RAG pipelines and where to be cautious when…
1 min read
Multi-Agent Systems: Orchestrating Specialized AI Agents
Complex enterprise tasks benefit from specialization. In my work, coordinating small specialist agents led to clearer reasoning, easier testing, and more…
1 min read
Agentic RAG Patterns: Self-Correcting and Adaptive Retrieval
When RAG systems can reason about what to retrieve, they stop failing silently. My implementations of agentic RAG show how to add evaluation and iterative…
1 min read
Building Production AI Assistants: Architecture and Design Patterns
Over the past year I've been part of several assistant projects that failed at scale not because the model was lacking, but because architecture and…
1 min read
OpenAI Assistants API: Production Patterns and Best Practices
I started building with the Assistants API in late 2023. In production it rewarded strict state management, clear tool contracts, and thoughtful thread…
1 min read
GPT-4 Turbo in Production: Lessons from Real-World Deployments
GPT 4 Turbo brings several improvements that matter for production: Feature GPT 4 GPT 4 Turbo Context Window 8K/32K 128K Input Cost $0.03/1K $0.01/1K Output…
2 min read
AI Predictions for 2024: What to Expect in Enterprise AI
As we enter 2024, the AI landscape is evolving at an unprecedented pace. After a transformative 2023 that brought us GPT-4, the Assistants API, and…
1 min read
Rate Limit Management for AI APIs
Tomorrow we'll explore token estimation techniques. Rate Limiting Patterns Circuit Breaker Pattern Azure OpenAI Best Practices
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Azure OpenAI Quotas: Understanding and Managing Limits
Tomorrow we'll explore rate limit management strategies. Azure OpenAI Quotas Quota Management Rate Limit Best Practices
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Data Analysis with AI: Practical Patterns and Techniques
The Code Interpreter release this month changed how I approach quick data analysis tasks. Not the deep, production-grade EDA that belongs in a Fabric…
1 min read
Azure OpenAI Code Interpreter: AI-Powered Data Analysis
Tomorrow we'll explore ChatGPT Code Interpreter capabilities. Azure OpenAI Assistants Code Interpreter Guide Azure OpenAI Pricing
1 min read
Circuit Breakers for AI Applications: Preventing Cascade Failures
This concludes our June 2023 series on Azure, Data, and AI topics. We covered: Microsoft Fabric deep dives (Lakehouse, Delta Lake, OneLake) Power BI Direct…
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Fallback Patterns for AI Applications
Fallback patterns ensure continuous service availability. Tomorrow, I will cover circuit breakers for AI applications.
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Retry Strategies for AI Applications
Azure OpenAI rate limits in mid-2023 are set per deployment per region, and hitting them is a normal operating condition rather than an exceptional one for…
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Error Handling for AI Applications: Building Resilient Systems
Robust error handling ensures AI applications remain useful even during failures. Tomorrow, I will cover retry strategies in more depth.
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Response Streaming: Real-Time LLM Output
Streaming improves the user experience significantly. Tomorrow, I will cover error handling for AI applications.
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Caching Strategies for LLM Applications
Effective caching significantly reduces LLM costs and latency. Tomorrow, I will cover response streaming patterns.
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Token Budgeting: Cost-Effective LLM Applications
Token costs for GPT-4 in mid-2023 are real enough to design around: roughly $0.03 per 1K input tokens and $0.06 per 1K output tokens for the 8K context…
1 min read
Context Pruning: Managing Token Limits in LLM Applications
Effective context pruning ensures your LLM applications work reliably. Tomorrow, I will cover token budgeting strategies.
1 min read
Conversation Management: Building Production Chat Systems
Robust conversation management enables reliable production chat systems. Tomorrow, I will cover context pruning strategies.
1 min read
Multi-Turn Conversations: Managing State in AI Applications
Multi-turn conversation management is essential for production chatbots. Tomorrow, I will cover conversation management strategies.
1 min read
Tool Use Patterns for AI Agents: Effective Function Design
The most consequential design decision in a function-calling agent isn't the model you choose — it's how you write the tool definitions. The model selects…
1 min read
Building AI Agents: From Chatbots to Autonomous Assistants
Function calling, released last week with the 0613 model versions, gives AI agents a proper foundation — and I've been rebuilding some agent prototypes to…
1 min read
Azure OpenAI Function Calling Patterns: Building Intelligent Agents
Function calling patterns enable sophisticated AI agents. Tomorrow, I will cover building AI agents in more depth.
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Azure AI Studio Preview: The Future of AI Application Development
Azure AI Studio provides a comprehensive environment for building, testing, and deploying AI applications. Tomorrow, I will cover Prompt Flow in more detail.
1 min read
Mastering Function Calling in Azure OpenAI: A Deep Dive
Function calling transforms GPT models into powerful agents that can interact with the real world. Tomorrow, I will cover Azure AI Studio in more detail.
1 min read
Azure OpenAI Updates at Build 2023: What Developers Need to Know
Azure OpenAI is becoming the enterprise-grade platform for building generative AI applications. Tomorrow, I will cover building plugins for ChatGPT.
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Pinecone Vector Database: Getting Started Guide
1. Use batching : Upsert in batches of 100+ for efficiency 2. Store text in metadata : Include searchable text in metadata 3. Use namespaces : Organize data…