2 min read
Evaluating LLM Outputs: Beyond Vibes
Production AI needs real evaluation. Here's how I approach it. You test with 5 prompts. They look good. Ship it.
Best Practices
130 articles
1 min read
AI Security: The Basics Everyone Misses
Most prompt injection attempts never get to your prompt if you filter input properly. Use managed identities. Always.
3 min read
Prompt Engineering Myths Killing Your AI Projects
"I need to be very detailed and explain everything thoroughly to get good results." No. You need to be clear, not verbose.
1 min read
Preparing Your Codebase for 2026: A Technical Debt Audit Guide
Technical debt compounds like financial debt. Regular audits and systematic paydown keep your codebase healthy and your team productive.
1 min read
API Design Patterns That Stood the Test of Time in 2025
Good API design is timeless. These patterns work because they prioritize developer experience and operational reliability.
1 min read
Infrastructure as Code Best Practices: Lessons from 100+ Deployments
IaC is a skill that compounds. These practices become second nature and save countless hours of debugging and recovery.
1 min read
Winter Solstice Code Review: My Most Impactful Refactors of 2025
On the longest night of the year, let's reflect on code improvements. Here are the refactoring patterns that delivered the biggest impact in my projects…
1 min read
The Ultimate Azure Cost Optimization Checklist for 2026
Start the new year with optimized cloud spend. These changes compound over time into significant savings.
1 min read
Securing AI Applications: A Comprehensive 2025 Checklist
AI security is not optional. Implement these measures before going to production, and review them quarterly as threats evolve.
1 min read
Performance Optimization for .NET 9 Applications on Azure
Results: Cold start reduced from 800ms to 150ms. These optimizations combined typically reduce Azure compute costs by 30-50% while improving response times.…
1 min read
Building Production RAG Systems: Best Practices for 2026
Production RAG is iterative. Start simple, measure everything, and improve based on real user feedback.
1 min read
Building Data Lakehouses: Medallion Architecture Best Practices
Each layer serves a specific purpose in the data refinement pipeline. Bronze captures raw data, silver cleans and conforms it, and gold delivers…
1 min read
Securing AI Applications: Authentication and Authorization Patterns
AI applications face unique security concerns: protecting training data, controlling model access, securing inference endpoints, and preventing prompt…
1 min read
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
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
September 2025 Recap: Key Takeaways for Enterprise AI
The most successful AI implementations share common architectural patterns. Hybrid search combining vector and keyword retrieval consistently outperforms…
1 min read
Prompt Injection Defense: Securing LLM Applications
Prompt injection occurs when user input is interpreted as instructions rather than data. Attackers can attempt to override system prompts, extract…
1 min read
Prompt Engineering: System Prompts That Actually Work
Invest time in crafting and testing your system prompts. They are the foundation of consistent, high-quality AI interactions.
1 min read
Prompt Engineering: Chain-of-Thought Techniques for Complex Reasoning
When faced with multi-step problems, LLMs perform better when prompted to reason step-by-step rather than jump directly to answers.
1 min read
Prompt Engineering Best Practices for Enterprise Applications
A well-structured prompt includes context, instructions, examples, and output format specifications.
1 min read
June Recap: Building Modern AI Systems
Understanding june recap is essential for production AI systems. Here's what you need to know.
1 min read
Data Lineage: Building Modern AI Systems
Understanding data lineage is essential for production AI systems. Here's what you need to know.
1 min read
Metadata Management: Building Modern AI Systems
Understanding metadata management is essential for production AI systems. Here's what you need to know.
1 min read
Data Governance: Building Modern AI Systems
Understanding data governance is essential for production AI systems. Here's what you need to know.
1 min read
Unity Catalog: Building Modern AI Systems
Understanding unity catalog is essential for production AI systems. Here's what you need to know.
1 min read
Iceberg Ai: Building Modern AI Systems
Understanding iceberg ai is essential for production AI systems. Here's what you need to know.
1 min read
Delta Lake Ai: Building Modern AI Systems
Understanding delta lake ai is essential for production AI systems. Here's what you need to know.
1 min read
Data Lakehouse Ai: Building Modern AI Systems
Understanding data lakehouse ai is essential for production AI systems. Here's what you need to know.
1 min read
Batch Processing: Building Modern AI Systems
Understanding batch processing is essential for production AI systems. Here's what you need to know.
1 min read
Caching Strategies: Building Modern AI Systems
Understanding caching strategies is essential for production AI systems. Here's what you need to know.
1 min read
Load Balancing Ai: Building Modern AI Systems
Understanding load balancing ai is essential for production AI systems. Here's what you need to know.
1 min read
Model Serving: Building Modern AI Systems
Understanding model serving is essential for production AI systems. Here's what you need to know.
1 min read
Gpu Optimization: Building Modern AI Systems
Understanding gpu optimization is essential for production AI systems. Here's what you need to know.
1 min read
Kubernetes Ai: Building Modern AI Systems
Understanding kubernetes ai is essential for production AI systems. Here's what you need to know.
1 min read
Container Apps Ai: Building Modern AI Systems
Understanding container apps ai is essential for production AI systems. Here's what you need to know.
1 min read
Durable Functions Ai: Building Modern AI Systems
Understanding durable functions ai is essential for production AI systems. Here's what you need to know.
1 min read
Azure Functions Ai: Building Modern AI Systems
Understanding azure functions ai is essential for production AI systems. Here's what you need to know.
1 min read
Serverless Ai: Building Modern AI Systems
Understanding serverless ai is essential for production AI systems. Here's what you need to know.
1 min read
Azure Event Grid Ai: Building Modern AI Systems
Understanding azure event grid ai is essential for production AI systems. Here's what you need to know.
1 min read
Kafka Ai: Building Modern AI Systems
Understanding kafka ai is essential for production AI systems. Here's what you need to know.
1 min read
Event Driven Ai: Building Modern AI Systems
Understanding event driven ai is essential for production AI systems. Here's what you need to know.
1 min read
Websocket Ai: Building Modern AI Systems
Understanding websocket ai is essential for production AI systems. Here's what you need to know.
1 min read
Streaming Inference: Building Modern AI Systems
Understanding streaming inference is essential for production AI systems. Here's what you need to know.
1 min read
Real Time Ai: Building Modern AI Systems
Understanding real time ai is essential for production AI systems. Here's what you need to know.
1 min read
AI Documentation: Documenting AI Systems Effectively
{promptconfig['template']} Comprehensive documentation ensures AI systems remain maintainable and understandable.
1 min read
AI Team Collaboration: Building Effective AI Development Teams
Effective AI teams combine diverse skills with clear collaboration patterns.
1 min read
AI Debugging Techniques: Diagnosing AI System Issues
Systematic debugging enables quick identification and resolution of AI issues.
1 min read
AI Testing Frameworks: Comprehensive Testing for AI Systems
Comprehensive AI testing ensures reliable behavior across diverse scenarios.
1 min read
Incident Response for AI Systems: Handling AI Failures
Effective incident response minimizes AI system impact and enables quick recovery.
1 min read
Model Versioning: Managing AI Model Lifecycle
Robust model versioning enables confident deployments and quick rollbacks.
1 min read
Prompt Management: Version Control for AI Instructions
Effective prompt management enables reliable AI applications with traceable changes.
1 min read
AI Security Best Practices: Protecting AI Systems
Comprehensive security protects AI systems from emerging threats.
1 min read
Responsible AI Practices: Implementing Ethical AI
Responsible AI practices build trust and ensure ethical AI deployment.
1 min read
AI Cost Optimization: Managing LLM Expenses
Strategic cost optimization can reduce AI expenses by 50-80% while maintaining quality.
1 min read
A/B Testing AI Features: Data-Driven AI Improvements
Rigorous experimentation enables confident AI improvements based on real user impact.
1 min read
Evaluation Metrics for AI: Measuring What Matters
Regular evaluation with consistent metrics drives continuous improvement in AI quality.
1 min read
Prompt Injection Defense: Protecting AI Applications
Defense in depth with multiple layers provides the best protection against prompt injection.
1 min read
Guardrails for LLMs: Building Safe AI Applications
Comprehensive guardrails are essential for responsible AI deployment.
1 min read
Structured Outputs: Getting Reliable Data from LLMs
Structured outputs enable reliable integration of LLM capabilities into data pipelines.
1 min read
Function Calling Patterns: Building Reliable AI Tools
Well-designed function calling creates powerful AI applications that interact safely with real systems.
1 min read
Context Window Management: Maximizing LLM Effectiveness
Strategic context management enables handling of complex, long-context scenarios.
1 min read
Chunking Strategies for RAG: Finding the Right Granularity
Choose chunking strategy based on document structure and retrieval requirements.
1 min read
AI-Powered Documentation: Keeping Docs in Sync with Code
AI-generated documentation reduces maintenance burden while improving quality.
1 min read
Testing AI Applications: Strategies for Non-Deterministic Systems
Robust AI testing combines deterministic checks with AI-powered evaluation.
1 min read
AI-Assisted Data Modeling: Accelerating Schema Design
AI-assisted modeling reduces design time while improving schema quality.
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
Documentation with AI: Automating Technical Writing for Data Projects
Documentation with AI: Automating Technical Writing for Data Projects
1 min read
Prompt Engineering 2025: Advanced Techniques for Better AI Outputs
Query: "What's the average order value by customer segment?" SQL: """ Effective prompt engineering is about clear communication. The better you describe…
1 min read
MLOps Maturity: Productionizing Machine Learning
MLOps is essential for sustainable ML in production. Start with experiment tracking and gradually add components as your ML practice matures.
1 min read
DataOps Maturity: Building Reliable Data Operations
DataOps maturity directly impacts data reliability and team productivity. Invest in these practices to build a robust data operation.
1 min read
Fabric Adoption Patterns: Lessons from Enterprise Implementations
Success with Fabric comes from treating it as a platform transformation, not just a technology deployment. Invest in people, process, and governance…
1 min read
AI Project Learnings: What Actually Works in Practice
These lessons were learned the hard way. Hopefully, they save you some pain.
1 min read
AI Agent Templates: Quick Start Patterns for Enterprise Use Cases
Templates accelerate agent development by providing battle-tested patterns. Start with a template, customize for your needs, and iterate based on real usage.
1 min read
Building AI Applications: Choosing the Right Platform and Architecture
The key to successful AI applications is choosing the right architecture for your requirements and building with production considerations from the start.
1 min read
Agent Safety Patterns: Building Trustworthy AI Systems
Safety in AI agents is not optional - it's foundational. Build safety in from the start, and your agents will be both powerful and trustworthy.
1 min read
Retry Strategies for AI Applications: Beyond Simple Backoff
Smart retry strategies balance reliability with resource efficiency. The key is adapting behavior based on error type, system state, and business requirements.
1 min read
Error Handling in LLM Applications: A Comprehensive Guide
Robust error handling is what separates prototypes from production systems. Invest in comprehensive error handling early to avoid painful debugging later.
1 min read
Agent Reliability Patterns: Building Robust AI Systems
Building reliable agents requires investment in error handling, recovery mechanisms, and observability. These patterns form the foundation for production AI…
1 min read
Building Type-Safe AI Applications: From Prompts to Production
Type safety transforms AI applications from fragile prototypes into robust production systems. Invest in types early - your future self will thank you.
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
Microsoft Fabric Governance: A Complete Guide
Effective Microsoft Fabric governance requires attention to access control, data quality, cost management, and lifecycle management. This month's…
1 min read
Microsoft Fabric June 2024: Month in Review
This month we explored Microsoft Fabric comprehensively. Today I'm summarizing key learnings and best practices from my June 2024 deep dive.
1 min read
Building Data Products in Microsoft Fabric
Data products are the deliverables of a data mesh. Today I'm exploring how to build, manage, and consume data products in Microsoft Fabric.
1 min read
Workspace Governance in Microsoft Fabric
Workspaces are the containers for collaboration in Fabric. Effective governance ensures organization, security, and efficiency. Today I'm covering workspace…
1 min read
AI Agent Best Practices: Lessons from Production
AI Agent Best Practices: Lessons from Production
2 min read
Microsoft Fabric Data Engineering: Best Practices After 6 Months in Production
Avoid over-partitioning. If partitions have < 1GB of data, consolidate. Use Data Pipelines, Not Just Notebooks
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…
1 min read
Fabric Performance Tuning: Optimizing Your Data Platform
Performance tuning in Fabric blends write-time optimisations (v-ordering, partitioning) with query-time strategies (predicate pushdown, materialised views).…
1 min read
Fabric Components: When to Use What
Fabric's component set is broad; the hard part is picking the minimal surface that solves your business need. This guide distils when to use Lakehouse…
1 min read
Microsoft Fabric Best Practices for Production
Shipping Fabric into production exposes common anti-patterns — unbounded capacity use, duplicated data copies, and insufficient observability. The best…
1 min read
Responsible AI Practices: Building Ethical AI Systems
Responsible AI isn't merely compliance box-ticking; it's product design that protects people and preserves trust. In practice I've found that pairing…
1 min read
Enterprise AI Lessons: What We Learned in 2023
Working with teams across finance, healthcare and retail this year, the same practical lessons come up: start with the business question, instrument for…
1 min read
AI Adoption Patterns: What Separates Success from Failure
After advising a range of enterprise AI programmes in 2023, the patterns are consistent: success comes from narrow business-first problem definition…
5 min read
LLM Application Testing: Strategies and Frameworks
Testing LLM applications is a problem that doesn't have a satisfying general-purpose solution yet, and that's worth acknowledging before diving into…
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
Microsoft Fabric Best Practices: Lessons Learned
Best practices and lessons learned from implementing Microsoft Fabric solutions for enterprise analytics.
1 min read
Fabric Workspace Setup: Organization and Best Practices
How you structure workspaces in Fabric matters more than most getting-started guides acknowledge. A workspace is the unit of security, capacity binding, and…
1 min read
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.
1 min read
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
Migrating to Microsoft Fabric: Strategies and Best Practices
The fastest approach uses OneLake shortcuts to reference existing data without copying. Run Fabric alongside existing platform during transition.
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
Embedding Best Practices for Production AI Systems
OpenAI Embeddings Guide MTEB Benchmark Embedding Best Practices
1 min read
Azure Security: A Year in Review
2022 brought significant security advancements in Azure, from Defender for Cloud improvements to enhanced Zero Trust capabilities. As we move into 2023,…
1 min read
Right-Sizing Azure Resources: A Data-Driven Approach
1. Collect metrics CPU, memory, network, IOPS 2. Analyze patterns Peak, average, trends 3. Identify candidates Under utilized resources 4. Recommend changes…
1 min read
Auto-Scaling Best Practices in Azure
Auto scaling is powerful but requires careful tuning. Start with conservative settings, monitor behavior, and adjust based on real data. Combine metric…
1 min read
Azure Spot Instance Strategies for Cost Savings
Spot VMs use Azure's excess capacity at significant discounts. Key characteristics: Up to 90% discount Can be evicted anytime 30 second eviction notice Best…
1 min read
Azure Cost Optimization Tips for 2023
Cost optimization is an ongoing practice, not a one time project. The combination of right sizing, reservations, storage optimization, and operational…
1 min read
FinOps Adoption: Managing Azure Costs Effectively
1. Teams need to collaborate : Finance, engineering, and business work together 2. Everyone takes ownership : Cost is everyone's responsibility 3. A…
1 min read
Developer Productivity Tools and Practices for 2023
The game changer for code generation: Beyond Copilot, ChatGPT helps with: Code explanation and review Documentation generation Debugging assistance Learning…
1 min read
DevOps Practices That Defined 2022
The four key metrics gained mainstream adoption: Security integrated earlier in the pipeline: IaC practices became more sophisticated: Three pillars…
1 min read
Model Cards: Documenting AI for Transparency and Trust
A model card is a documentation framework for machine learning models, inspired by nutrition labels on food. It provides essential information about a…
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…
1 min read
Black Friday Azure Tips: Cost Optimization for Peak Traffic
Don't wait for auto scaling to catch up during traffic spikes: Reduce database load with intelligent caching: Use read replicas and query optimization:…
1 min read
Azure DevOps Best Practices - October 2022 Summary
Azure DevOps provides a comprehensive platform for enterprise DevOps with robust security and governance features.
1 min read
GitHub Reusable Workflows Patterns
Reusable workflows reduce duplication and improve maintainability across repositories.
1 min read
Designing End-to-End IoT Solution Architecture on Azure
This architecture provides a scalable, secure foundation for enterprise IoT solutions on Azure.
1 min read
Azure IoT Security Best Practices
Implementing these security best practices creates a defense-in-depth approach for your IoT solutions.
1 min read
Azure Advisor Score: Measuring Your Cloud Excellence
Azure Advisor Score provides actionable insights to continuously improve your Azure environment's health and efficiency.
1 min read
Azure Well-Architected Reviews: Building Better Workloads
Regular Well-Architected Reviews ensure your workloads remain optimized across all five pillars as they evolve.
1 min read
Cloud Adoption Framework: Your Guide to Azure Success
The Microsoft Cloud Adoption Framework (CAF) is the comprehensive guidance body that addresses the full journey of cloud adoption—not just the technical…
1 min read
DataOps Practices: Agile for Data Teams
DataOps in 2021 matured from concept to standard practice. Teams that adopted these practices delivered faster and more reliably than those stuck in manual…
1 min read
Data Quality Practices: Building Trust in Your Data
Data quality in 2021 became an engineering discipline. The tools matured, but success requires organizational commitment to treating data as a product.
1 min read
Cost Optimization Strategies for Azure Workloads
Cost optimization in 2021 became a core cloud competency. The tools are powerful; success requires discipline and cultural change.
1 min read
Azure Automanage: Automated VM Management Best Practices
Azure Automanage transforms VM management from a series of manual tasks into an automated, best-practice-driven process. It's ideal for organizations that…
2 min read
Job Clusters vs All-Purpose Clusters: Choosing the Right Approach
The job cluster versus all-purpose cluster decision in Databricks is primarily a cost decision: all-purpose clusters stay running between tasks (you pay for…
1 min read
Leveraging Azure Architecture Center Reference Architectures
The Azure Architecture Center is the reference resource I bookmark more than any other when I need to move from "I know what I want to build" to "I know the…
1 min read
Implementing the Azure Well-Architected Framework
The Azure Well-Architected Framework is the document I send to clients at the start of every architecture engagement and the checklist I review before every…
1 min read
Implementing Azure Advisor Recommendations at Scale
Azure Advisor is the free recommendation service that surfaces findings your team should have already found—and usually hasn't. The five categories (Cost…
1 min read
Azure Storage Account Security Best Practices
Storage accounts are deceptively easy to deploy and easy to misconfigure into a leak. Public blob containers are still the most common "how did this get…