2 min read
Azure OpenAI vs OpenAI: Choosing the Right Path
After building production systems on both, here's the honest comparison. Enterprise compliance. Your data stays in your Azure tenant. No data sent to…
OpenAI
143 articles
3 min read
Azure OpenAI: The Hidden Costs Nobody Talks About
Let me show you where the costs hide. Simple math, right? Wrong. Every chat conversation includes the entire history. That "helpful" feature where the AI…
1 min read
The Complete Guide to AI Model Fine-Tuning on Azure
Fine-tuning is powerful but expensive. Validate the ROI before investing in training infrastructure.
1 min read
Azure OpenAI vs OpenAI Direct: A 2025 Comparison Guide
One of the most common questions I receive from enterprise clients is whether to use Azure OpenAI Service or OpenAI's API directly. After working with both…
1 min read
Embedding Models: Choosing Between OpenAI, Azure, and Open Source
The text-embedding-ada-002 and newer text-embedding-3 models provide high-quality embeddings with minimal setup.
1 min read
Azure OpenAI Best Practices: Production-Ready AI Applications
Azure OpenAI provides enterprise capabilities, but you need to build robust infrastructure around it for production use.
1 min read
Reasoning Models Evolution: From o1 to o3 and Beyond
Reasoning models represent a significant advancement in AI capability. Use them for problems that truly require deep thinking, and you'll see dramatically…
1 min read
GPT-5 Predictions: What to Expect from OpenAI's Next Frontier Model
The jump from GPT-4 to GPT-5 will likely be significant. Organizations that prepare now will be able to leverage new capabilities immediately upon release.
1 min read
Tool Choice Control: Fine-Tuning When and How AI Uses Tools
Tool choice control is essential for building AI applications that are predictable, safe, and aligned with your business logic. Use these patterns to guide…
1 min read
Parallel Function Calling: Execute Multiple Tools Simultaneously
Parallel function calling dramatically improves response times when multiple independent operations are needed. Use it wisely to build faster, more…
1 min read
Function Calling Updates: What's New in September 2024
Function calling is the foundation of agentic AI. These patterns help you build reliable, type-safe tool integrations that scale.
1 min read
Pydantic Integration with OpenAI: Type-Safe AI Development
Pydantic integration makes OpenAI's structured outputs truly type-safe, catching errors at development time and ensuring your AI-generated data is always valid.
1 min read
JSON Schema Enforcement in LLM Applications
JSON schema enforcement transforms unpredictable LLM outputs into reliable, type-safe data that your applications can trust.
1 min read
Structured Outputs: Reliable JSON from OpenAI Models
Structured Outputs transform unreliable text generation into dependable data extraction. Use them whenever you need guaranteed JSON structure from your AI…
1 min read
OpenAI API Updates: September 2024 Changes You Need to Know
o1 introduces new parameters while removing some familiar ones: Feature GPT 4o o1 preview System messages Yes No Streaming Yes No Function calling Yes No…
1 min read
Advanced Reasoning Techniques on Azure OpenAI
The AI landscape is evolving rapidly. OpenAI has hinted at improved reasoning capabilities in future models. Build your infrastructure to be flexible and…
1 min read
Mathematical Reasoning with o1: From Basics to Proofs
Mathematical reasoning is one of o1's strongest capabilities. Use it for problems that require genuine reasoning, not just computation.
1 min read
Multi-Step Reasoning Patterns with o1
Multi-step reasoning is o1's superpower. Use these patterns to unlock its full potential.
1 min read
Complex Problem Solving with o1: Practical Patterns
o1's reasoning capabilities open up new possibilities for tackling complex problems that were previously difficult for AI to handle reliably.
1 min read
Thinking Tokens: Understanding o1's Hidden Reasoning Process
Understanding thinking tokens helps you make informed decisions about when o1's extended reasoning is worth the investment.
1 min read
When to Use o1: A Decision Guide for AI Applications
o1 shines in specific scenarios. Understanding these helps you deploy it effectively.
1 min read
Choosing the Right Model: GPT-4o vs Claude 3.5 Sonnet for Different Tasks
Based on published benchmarks: Task GPT 4o Claude 3.5 Sonnet MMLU 88.7% 88.7% HumanEval 90.2% 92.0% MATH 76.6% 71.1% Graduate Reasoning 65% 59.4% The model…
1 min read
Chain-of-Thought Prompting: Improving AI Reasoning Today
OpenAI and other labs are likely working on models with built-in reasoning capabilities. When these arrive, they may reduce the need for explicit CoT prompting.
2 min read
The Future of Reasoning in AI: What's Coming Next
This could dramatically improve performance on complex tasks. The next breakthrough in AI capabilities is likely to come from better reasoning, not just…
2 min read
Claude 3.5 Sonnet vs GPT-4o: A Technical Comparison
With Claude 3.5 Sonnet and GPT-4o both available, choosing the right model for your application requires understanding their differences. I've been testing…
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…
2 min read
GPT-4o Released: OpenAI's Fastest and Most Capable Multimodal Model
Today OpenAI announced GPT-4o (the "o" stands for "omni") - their new flagship model that can reason across audio, vision, and text in real time. This is…
1 min read
GPT-4o Speed Improvements: Building Low-Latency Applications
GPT-4o is 2x faster than GPT-4 Turbo. Today I'm exploring how to leverage this speed for responsive applications.
1 min read
Cost Reduction Strategies with GPT-4o
GPT-4o is already 50% cheaper than GPT-4 Turbo, but there are more ways to optimize costs. Here are practical strategies I use in production.
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…
1 min read
Multimodal Conversations with GPT-4o
A typical multimodal conversation might look like: \n{code snippet}\n For real time applications: Managing context across modalities: 1. Maintain coherent…
1 min read
GPT-4o Real-Time Vision: Processing Images at Scale
GPT-4o's vision capabilities are impressive, but what makes them practical for enterprise is the combination of quality, speed, and cost. Today I'm…
1 min read
Building Voice AI Applications with Azure OpenAI
Voice AI is transforming how we interact with applications. Today I'm exploring how to build voice-enabled AI applications using Azure's current capabilities.
1 min read
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.
1 min read
Microsoft Build 2024 Preview: What to Expect for AI
Microsoft has been rapidly expanding Azure OpenAI capabilities. We can expect: New model deployments : Potentially GPT 4 improvements or new multimodal…
1 min read
Azure OpenAI Fine-Tuning GA: Production-Ready Customization
Azure OpenAI fine-tuning is now generally available, bringing enterprise-grade model customization to the Azure platform. This comprehensive guide covers…
1 min read
Model Fine-Tuning Comparison: OpenAI vs Azure vs Open Source
Fine-tuning allows you to customize LLMs for specific tasks. This guide compares the major approaches available today.
2 min read
Claude 2.1 vs GPT-4: A Technical Comparison While We Await Claude 3
With Claude 3 expected soon, now is a good time to compare the current state of play between Claude 2.1 and GPT-4. Let's dive into a technical comparison of…
1 min read
Sora Announcement Impact: What OpenAI's Video Model Means for Enterprise
Sora is OpenAI's text to video model capable of generating up to 60 second videos from text prompts with: High visual fidelity Complex scene understanding…
1 min read
Custom GPTs for Enterprise: Building Internal AI Tools
As organizations hand Custom GPTs to teams, my focus has been on governance, access control, and sensible defaults. These notes outline how enterprises can…
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
Assistants Retrieval Tool: Building Knowledge Assistants
For many teams, the Assistants API's Retrieval tool removes the most tedious parts of building a knowledge assistant — you upload documents and the…
1 min read
Code Interpreter: AI-Powered Data Analysis
Code Interpreter (Advanced Data Analysis) is the single most productive AI tool I've used for exploratory analysis — it runs Python in a sandbox, opens…
1 min read
Assistants API: File Handling and Knowledge Retrieval
I prefer the Assistants API's file-based retrieval when I need a fast, low-friction knowledge assistant — upload a set of documents and let the assistant…
1 min read
Assistants API Deep Dive: Threads and Messages
Threads and Messages are the state management primitives in the Assistants API, and understanding how they work changes how you architect multi-turn…
1 min read
Azure OpenAI Assistants API: Enterprise AI Agents
The Azure OpenAI Assistants API — launched in preview on Azure shortly after the OpenAI DevDay announcement in November 2023 — is the stateful AI agent…
1 min read
Enhanced Function Calling in GPT-4 Turbo
Parallel function calling in GPT-4 Turbo is the DevDay improvement to function calling that changes what's architecturally practical with AI agents.…
2 min read
Seed Parameter: Achieving Reproducible LLM Outputs
The seed parameter introduced in GPT-4 Turbo at DevDay 2023 is a useful addition for testing and debugging, but it's important to understand what…
1 min read
JSON Mode: Reliable Structured Outputs from GPT-4
JSON mode in GPT-4 Turbo (gpt-4-1106-preview) is a simple addition that solves a real, persistent annoyance in LLM application development: the model…
3 min read
OpenAI DevDay 2023: Revolutionary Announcements for AI Developers
OpenAI DevDay happened on November 6, 2023 in San Francisco — the first developer conference from a company that, two years ago, didn't exist as a…
2 min read
Building RAG Applications with Azure Cognitive Search and OpenAI
RAG with Azure Cognitive Search and Azure OpenAI is the production architecture I recommend most often for enterprise knowledge retrieval — not because it's…
2 min read
Prompt Engineering Mastery: Advanced Techniques for GPT-4
GPT-4 has been available since March 2023 and the gap between practitioners who've put in real work with it and those still writing prompts by intuition is…
1 min read
Azure OpenAI Enterprise Patterns: Lessons from Production Deployments
After months of working with Azure OpenAI in enterprise environments, the patterns that separate solid deployments from problematic ones have become clear …
1 min read
Meeting Summarization with Azure AI
Build intelligent meeting summarization solutions using Azure AI Speech and OpenAI services.
1 min read
Image Generation with DALL-E 2 on Azure OpenAI Service
Learn how to generate images using DALL-E 2 on Azure OpenAI Service for creative and business applications.
1 min read
LangChain with Azure OpenAI: Building Production AI Applications
Learn how to use LangChain with Azure OpenAI Service to build robust, production-ready AI applications.
1 min read
Maximizing GPT-4's Context Window: Patterns for Large Document Processing
Learn effective strategies for working within GPT-4's context window limits and processing large documents with Azure OpenAI Service.
1 min read
Semantic Kernel: Building AI-Powered Applications with .NET
Learn how to use Microsoft's Semantic Kernel SDK to build intelligent applications that leverage GPT-4 and Azure OpenAI Service.
1 min read
Token Estimation: Predicting API Costs
Tomorrow we'll explore prompt compression techniques. tiktoken Library OpenAI Tokenizer Azure OpenAI Pricing
1 min read
Building Plugins for ChatGPT: Extending AI with Custom Tools
ChatGPT plugins extend AI capabilities with real-time data and actions. Tomorrow, I will cover function calling patterns in more depth.
1 min read
AI-Powered Data Quality: Intelligent Data Validation
AI-powered data quality goes beyond static rules. Intelligent systems understand context, detect subtle anomalies, and provide actionable recommendations…
1 min read
Azure Synapse Analytics + OpenAI: Intelligent Data Warehousing
Azure Synapse with OpenAI transforms data warehousing from technical SQL expertise to conversational data access. Business users can explore enterprise data…
1 min read
Building AI Data Analysis Assistants
AI data analysis assistants make insights accessible to everyone, regardless of technical expertise. The key is combining natural language understanding…
1 min read
LLM-Powered SQL Generation: Patterns and Practices
LLM-powered SQL generation democratizes data access. With proper validation and safety measures, it enables anyone to query databases using natural language.
1 min read
Azure Databricks + OpenAI: LLMs for Data Engineering
Azure Databricks with Azure OpenAI creates intelligent data platforms where natural language becomes the interface for data engineering tasks.
1 min read
Integrating Azure Machine Learning with Azure OpenAI
The combination of Azure ML's operational capabilities with Azure OpenAI's language understanding creates powerful, production-ready AI systems.
1 min read
Model Deployment Patterns for Azure OpenAI
These deployment patterns enable safe, controlled rollouts of LLM application changes. Start with gateway and blue-green patterns, then add canary and A/B…
1 min read
Introduction to LLMOps: MLOps for Large Language Models
LLMOps brings discipline to LLM application development. Start with these foundations and iterate as your applications mature.
1 min read
Azure OpenAI Fine-Tuning: Preview and Patterns
Fine-tuning is powerful but requires careful data preparation and evaluation. Start with prompt engineering, use RAG when you need sources, and fine-tune…
1 min read
Map-Reduce Patterns for LLM Applications
Map-reduce transforms complex LLM tasks into manageable, parallelizable operations. Master these patterns for processing data at any scale.
1 min read
Recursive Summarization for Long Documents
Recursive summarization enables processing of documents of any length while maintaining quality through iterative refinement.
1 min read
Document Summarization Patterns with GPT-4
Effective summarization adapts to document type, size, and audience needs. These patterns provide a foundation for production-ready summarization systems.
1 min read
Context Window Management: 8K vs 32K Strategies
The 32K model costs 2x more per token. Use it strategically. Effective context management balances quality, cost, and capability. Master these patterns to…
1 min read
Token Optimization Strategies for GPT-4
Output tokens cost 2x input tokens. This changes optimization strategy. With systematic token optimization, you can reduce GPT-4 costs by 50-70% while…
1 min read
GPT-4 Limitations and Workarounds
GPT-4's training data ends in September 2021. It doesn't know about recent events, technologies, or updates.
1 min read
Building a GPT-4 Writing Assistant
GPT-4's writing capabilities go far beyond autocomplete. With proper prompting and structure, it becomes a powerful writing partner for technical content…
1 min read
GPT-4 for Data Analysis: Practical Applications
GPT-4 transforms data analysis from technical SQL writing to conversational exploration. The ability to interpret results and generate insights makes data…
1 min read
Building GPT-4 Powered Coding Assistants
Provide 3 possible completions. Return ONLY the code to insert, no explanation. Format as JSON: ["completion1", "completion2", "completion3"]"""
4 min read
GPT-4 is Here: What Changes for Enterprise AI
The revelation that landed with GPT-4's launch wasn't just the capability improvement—it was learning that Microsoft's new Bing Chat had been running on…
1 min read
GPT-4 Enterprise Use Cases
GPT-4's cost (~$0.10 per analysis) is negligible compared to time savings. GPT-4 enables automation of knowledge work that was previously too complex for…
1 min read
GPT-4 on Azure OpenAI: What to Expect
Stay updated through Azure OpenAI documentation and Microsoft announcements.
1 min read
GPT-4 Multimodal: Understanding Vision Capabilities
GPT-4 Vision opens new categories of applications. Start planning your use cases now so you're ready when access becomes available.
4 min read
GPT-4 vs GPT-3.5: A Practical Comparison
After the first day of GPT-4 access, my comparison methodology was deliberately practical rather than benchmark-focused: I ran the same set of prompts—tasks…
2 min read
GPT-4 Is Here: Analyzing the Announcement
This isn't just incremental improvement - it's a capability threshold crossing. The AI capability curve just jumped. Time to adapt.
2 min read
Preparing for GPT-4: What We Know So Far
On March 13, 2023—the day before OpenAI announced GPT-4—the AI practitioner community was in an unusual state: highly confident that something significant…
1 min read
Enterprise Patterns for Azure OpenAI Applications
Deploy across regions for resilience: Configure Azure API Management for governance: Enterprise Azure OpenAI deployments need: 1. Multi region failover for…
1 min read
Streaming Responses with Azure OpenAI
Streaming transforms AI applications from feeling sluggish to feeling responsive. The implementation adds complexity, but the UX improvement is substantial.…
1 min read
Azure OpenAI Cost Optimization Strategies
One token is roughly 4 characters in English. A 1000-word document is about 1300 tokens. Start with these strategies and refine based on your usage…
1 min read
Understanding ChatGPT Plugins Architecture
The plugin ecosystem is just beginning. Now is the time to experiment and understand the patterns.
1 min read
Responsible AI Practices with Azure OpenAI
Responsible AI isn't just about compliance - it's about building trust with users and ensuring AI benefits everyone. Azure OpenAI provides a foundation, but…
1 min read
LangChain with Azure OpenAI: Getting Started
Combined with Azure OpenAI's enterprise security and compliance, it's a powerful combination.
1 min read
Implementing RAG with Azure Cognitive Search
RAG is the bridge between general-purpose LLMs and your specific enterprise data. Get it right, and you unlock tremendous value.
1 min read
Prompt Engineering Fundamentals for Azure OpenAI
Provide specific line-by-line feedback.""", requiredvars=["databasetype", "query"] ) prompt = SQLREVIEWTEMPLATE.format( databasetype="Azure SQL Database"…
1 min read
Building Semantic Search with Azure OpenAI Embeddings
Embeddings are numerical representations of text that capture semantic meaning. Similar concepts have similar vectors. Azure OpenAI provides the…
1 min read
Semantic Kernel Memory: Building AI with Long-Term Context
1. Organize into collections : Separate by topic/domain 2. Use meaningful IDs : Enable updates and deletions 3. Set appropriate relevance thresholds : Too…
1 min read
Semantic Kernel Planners: Automatic AI Orchestration
Creates multi step plans: Selects the single best action: Iteratively reasons through complex problems: 1. Register clear skill descriptions : Planners rely…
1 min read
Semantic Kernel Plugins: Extending AI with Custom Skills
1. Single responsibility : Each plugin should do one thing well 2. Clear descriptions : Help planners understand what functions do 3. Validate inputs :…
1 min read
Semantic Kernel Introduction: Microsoft's AI Orchestration SDK
Define AI functions using natural language prompts: Create reusable prompts: Add Python functions as skills: Combine functions into pipelines: Store and…
1 min read
LangChain Introduction: Building AI Applications with Python
Create reusable, parameterized prompts: Combine components into workflows: Load content from various sources: Split documents for embedding: Generate…
1 min read
Embedding Best Practices for Production AI Systems
OpenAI Embeddings Guide MTEB Benchmark Embedding Best Practices
1 min read
Document Chunking Strategies for RAG Systems
Simple but effective for uniform content: Respect sentence boundaries: Natural document structure: Use embeddings to find natural break points: Hierarchical…
1 min read
RAG Architecture Patterns: Building Production-Ready Systems
The basic pattern for straightforward use cases: Transform queries for better retrieval: Generate a hypothetical answer first, then use it for retrieval:…
1 min read
Introduction to Retrieval Augmented Generation (RAG)
RAG solves both by retrieving relevant context before generating responses.
1 min read
Building Semantic Search with Azure OpenAI Embeddings
Combine semantic search with keyword matching: Expand queries for better recall: 1. Pre compute embeddings : Don't embed at query time for documents 2. Use…
1 min read
Introduction to Text Embeddings with Azure OpenAI
Embeddings are dense vector representations of text where: Similar meanings are close together in vector space Different meanings are far apart…
1 min read
Streaming Responses with Azure OpenAI: Real-Time AI Output
Without streaming, users wait for the entire response: 500 tokens at 50 tokens/second = 10 seconds of waiting Users see nothing, then everything at once…
1 min read
Azure OpenAI REST API: Direct Integration Without SDKs
Azure OpenAI REST API follows this pattern: Two authentication methods are available: Always specify the API version: 1. Always handle errors : Check status…
3 min read
Azure OpenAI Service is Now Generally Available
Azure OpenAI Service gives you API access to OpenAI's models - GPT-3.5, Codex, and DALL-E - but running on Azure infrastructure with enterprise-grade security.
1 min read
Azure OpenAI .NET SDK: Building Enterprise AI with C#
1. Use Azure AD authentication for production 2. Implement retry policies with Polly 3. Use dependency injection for service management 4. Stream responses…
2 min read
Azure OpenAI Python SDK: A Complete Developer Guide
The Python SDK for Azure OpenAI (openai package with Azure-specific configuration) is the de facto starting point for Azure OpenAI development—the largest…
1 min read
Completion vs Chat APIs in Azure OpenAI: Choosing the Right Approach
The original API for text generation: Message based API with roles: Create an abstraction that works with both APIs:
2 min read
Temperature and Top-P: Fine-Tuning Azure OpenAI Response Creativity
Temperature controls randomness in token selection: Temperature = 0 : Nearly deterministic, always picks the most likely token Temperature = 1 : Standard…
1 min read
System Prompts in Azure OpenAI: Controlling AI Behavior
System prompts are special messages that set the context for the entire conversation: A well structured system prompt includes: Build a library of reusable…
1 min read
Chain-of-Thought Prompting: Making LLMs Reason Step by Step
Chain-of-thought prompting encourages models to break down problems into intermediate reasoning steps before giving a final answer.
1 min read
Few-Shot Learning with Azure OpenAI: Teaching by Example
Few shot learning provides examples that demonstrate the desired input output pattern: Zero shot : No examples, just instructions One shot : One example Few…
2 min read
Prompt Engineering Fundamentals for Azure OpenAI
Prompt engineering in January 2023 was the skill that separated AI features that worked in demos from AI features that worked in production. The gap: a demo…
1 min read
Token Management in Azure OpenAI: Counting, Optimizing, and Budgeting
Tokens are the basic units of text that LLMs process. In English: 1 token ≈ 4 characters 1 token ≈ 0.75 words 100 tokens ≈ 75 words Build a comprehensive…
1 min read
Rate Limiting and Throttling with Azure OpenAI Service
Azure OpenAI uses Tokens Per Minute (TPM) as the primary quota metric: Model Default TPM Max TPM (with increase) GPT 3.5 Turbo 120K 300K+ Text Davinci 003…
1 min read
Content Filtering in Azure OpenAI: Implementing Safety Guardrails
Each category has severity levels: safe, low, medium, high.
1 min read
Responsible AI with Azure OpenAI: Building Ethical AI Systems
Microsoft's Responsible AI framework guides Azure OpenAI Service: 1. Fairness : AI systems should treat all people fairly 2. Reliability & Safety : AI…
2 min read
Azure OpenAI vs OpenAI API: Which One Should You Choose?
The right choice depends on your specific requirements. For most enterprise scenarios, Azure OpenAI's security and compliance features make it the clear…
2 min read
Enterprise AI with Azure OpenAI: Security, Compliance, and Governance
The question I kept hearing from enterprise clients in January 2023 was some variation of: "We've seen what ChatGPT can do—how do we get that capability…
1 min read
ChatGPT Integration Patterns for Enterprise Applications
Until then, these patterns will help you build production-ready chat experiences.
1 min read
GPT-3.5 on Azure: Understanding the Model and Its Capabilities
GPT 3.5 is not a single model but a family of models with different capabilities: Model Best For Max Tokens Cost text davinci 003 Complex tasks, longer…
2 min read
Azure OpenAI Service GA Announcement: A New Era for Enterprise AI
2023 is going to be the year AI goes mainstream in enterprise applications. Azure OpenAI Service removes the last major barrier - security and compliance…
2 min read
AI/ML Milestones of 2022: A Technical Retrospective
Significant improvements in instruction following through RLHF. Art-focused generation with distinctive aesthetic quality.
4 min read
ChatGPT: What OpenAI's New AI Chatbot Means for Developers
One week after ChatGPT's launch and it had crossed one million users—a milestone that took Netflix 3.5 years, Facebook 10 months, and Instagram 2.5 months.…
1 min read
Prompt Engineering Basics: Getting Better Results from AI
Poor prompt: "Write code for a website" Better prompt: "Write HTML and CSS for a responsive landing page for a SaaS product. Include a hero section with…
3 min read
ChatGPT in Developer Workflows: Practical Applications
Twenty-four hours after ChatGPT launched, my developer group chats were a stream of screenshots—developers sharing prompts and responses the way we used to…
4 min read
ChatGPT Launches: The AI Revolution Begins Today
ChatGPT launched on November 30, 2022, and I spent most of that day in a state of barely contained astonishment—not because a chatbot was impressive (I'd…
1 min read
Advanced GPT-3 Prompt Engineering: Preparing for the Next Wave of AI
The trend is clear: models are becoming more capable at following instructions and engaging in dialogue-like interactions.
1 min read
Azure Cognitive Services Updates: OpenAI Integration and More
Cognitive Services now includes Azure OpenAI Service, bringing GPT models into the Cognitive Services family: The new Image Analysis API includes enhanced…
1 min read
Building LLM Applications with Azure OpenAI: Patterns and Practices
When building applications with GPT 3 and other LLMs, you need to think about: Prompt design and management Chaining multiple LLM calls Testing and…
1 min read
DALL-E 2 on Azure: AI Image Generation for Enterprise
DALL E 2 creates realistic images from natural language descriptions. It can: Generate original images from text prompts Edit existing images based on…
5 min read
Using Codex Models for Azure Development Automation
return self.generate(prompt, maxtokens=500, temperature=0.3) return self.generate(prompt, maxtokens=500, temperature=0.2)
3 min read
GPT-3 Models on Azure: Understanding text-davinci-002 and Codex
Provide the optimized query with explanations.""" ERRORDIAGNOSIS = """Diagnose this error: Error: {error} Context: {context}
3 min read
Azure OpenAI Service Updates at Microsoft Ignite 2022
Azure OpenAI Service is moving toward broader preview access, with more customers being able to apply and get approved. The application process is becoming…
2 min read
GitHub Copilot is Now Generally Available: The AI Pair Programmer for Everyone
GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It uses the Codex model (a descendant of GPT-3…
4 min read
Codex Models: AI-Powered Code Generation for Developers
response = openai.Completion.create( engine="code-davinci-002", prompt=prompt, maxtokens=300, temperature=0.3 )
1 min read
GPT-3 for Enterprise: Practical Applications and Implementation Patterns
One of the most immediate applications - turning lengthy documents into concise summaries.
3 min read
Azure OpenAI Service Expansion: Bringing GPT Models to Enterprise
This isn't just "OpenAI but on Azure" - it's OpenAI made enterprise-ready. The approval process exists because Microsoft wants to ensure responsible use. Be…
3 min read
Azure OpenAI Service Announced: What We Know About the Limited Preview
This announcement is exciting for the enterprise AI space. The combination of OpenAI's powerful models with Azure's enterprise infrastructure addresses a…
2 min read
Exploring the OpenAI GPT-3 API: Practical Patterns and Techniques
This is direct OpenAI access - enterprise Azure integration may come in the future. response = openai.Completion.create( engine="text-davinci-002"…