1 min read
Building a Personal AI Assistant with Semantic Kernel
This foundation can be extended with calendar integration, email access, and more plugins. Perfect for a holiday coding project!
Python
61 articles
1 min read
Migrating from LangChain to Semantic Kernel: A Practical Guide
Many teams that started with LangChain in 2024 are now evaluating Semantic Kernel for its tighter Azure integration and production-ready features. Having…
1 min read
Testing LLM Applications: Strategies Beyond Traditional Unit Tests
LLM outputs vary between runs and model versions. Testing must focus on behavioral properties rather than exact string matching, while still catching…
1 min read
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…
1 min read
Azure Document Intelligence: Table Extraction and Analysis
Table extraction transforms static reports into queryable data. Combine extracted tables with LLM analysis to answer questions about financial statements…
1 min read
Azure AI Document Intelligence: Extracting Structured Data from Invoices
First, provision an Azure AI Document Intelligence resource and configure your client. The prebuilt invoice model recognizes vendor details, line items…
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.
1 min read
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…
1 min read
Building Production MLOps Pipelines with Azure Machine Learning
Automated pipelines ensure reproducibility, enable A/B testing, and provide audit trails for model governance.
1 min read
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 AI-Powered Search with Azure AI Search and Semantic Ranker
Monitor reranker scores and caption extraction quality. A/B test semantic ranking against pure vector or keyword search to quantify improvements for your…
1 min read
Multimodal RAG: Combining Text, Images, and Tables for Enhanced Search
Multimodal RAG ensures users find relevant information regardless of how it's represented in the source documents.
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Structured Output with JSON Mode: Reliable Data Extraction from LLMs
Structured outputs eliminate parsing errors. Add business validation for semantic correctness - the schema ensures format, you ensure meaning.
1 min read
Implementing Semantic Caching for LLM Applications
Use Redis Vector Search for efficient similarity matching at scale. Tune the similarity threshold based on your use case - higher values ensure more precise…
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.
1 min read
Azure Functions with AI: Serverless Inference Patterns
Use consumption-based billing to pay only for actual inference time. Implement caching for repeated queries and batch similar requests to amortize cold…
1 min read
Claude Extended Thinking: Solving Complex Reasoning Problems
Enable extended thinking for mathematical proofs, code analysis, strategic planning, and multi-constraint optimization problems. For simple queries…
1 min read
Azure Machine Learning Managed Endpoints: Deploying Models with Zero Downtime
Configure Azure Monitor alerts on latency and error rates. Implement automatic rollback triggers when thresholds are breached.
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Implementing Guardrails for Production LLM Applications
Guardrails are essential for enterprise AI deployments, protecting both users and your organization.
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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…
1 min read
Evaluating RAG Systems: Metrics and Automated Testing with Azure AI
Fail builds when quality metrics drop below thresholds, catching regressions before they reach production.
1 min read
Building Multi-Agent Systems with Azure AI Foundry Orchestration
Azure AI Foundry provides trace visualization showing the complete agent interaction graph, making it easy to debug complex workflows and optimize handoff…
1 min read
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.
1 min read
Cosmos DB Vector Search: Implementing Semantic Search at Global Scale
Configure your Cosmos account with write regions near your users and read replicas globally. Vector searches automatically route to the nearest replica…
1 min read
Real-Time AI Inference with Azure Container Apps: Dynamic Scaling Patterns
Use spot instances for non-critical workloads, implement request batching for throughput optimization, and configure appropriate scale-to-zero policies.…
1 min read
Azure AI Search Vector Optimization: Reducing Costs While Improving Recall
Vector search costs can spiral quickly at scale. After optimizing Azure AI Search deployments processing 50 million vectors, I've identified key patterns…
1 min read
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…
1 min read
Claude 3.5 Sonnet for Code Review: Automated PR Analysis Patterns
Claude 3.5 Sonnet has become my go-to model for automated code review workflows. Its exceptional ability to understand context across large codebases makes…
1 min read
Building AI Agents with Azure AI Foundry: A Post-Build 2025 Implementation Guide
The new agent framework introduces several critical improvements. First, native multi-agent orchestration allows agents to collaborate without custom…
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
Reliable Data Extraction with LLMs: Patterns and Practices
Reliable extraction requires careful schema design, multi-pass validation, and explicit handling of uncertainty. These patterns help you build extraction…
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.
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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
Conditional Edges in LangGraph: Dynamic Agent Flows
Route based on multiple state attributes: Sometimes you don't know all routes ahead of time: Multiple decision points in sequence: Route with randomization…
1 min read
LangGraph Introduction: Building Stateful AI Agents
LangGraph provides these capabilities through a graph-based execution model.
1 min read
LangChain Updates: What's New in July 2024
LangChain 0.2.x Stability LCEL (LangChain Expression Language) Maturity Azure AI Search Vector Store
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Fabric SDK: Python Development for Microsoft Fabric
The Microsoft Fabric SDK provides a Pythonic interface for working with Fabric services. This guide covers installation, configuration, and common…
1 min read
CrewAI Introduction: Role-Based Multi-Agent Framework
I started experimenting with CrewAI because I wanted clearer role definitions in multi-agent workflows. CrewAI's focus on roles, goals, and agent…
1 min read
AutoGen Introduction: Microsoft's Multi-Agent Framework
I've used AutoGen to prototype multi-agent workflows that handle planning, tool use, and execution. This practical introduction focuses on the patterns that…
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
Understanding Runnable Interfaces in LangChain
Deep dive into LangChain's Runnable interface and its implementations for building flexible LLM pipelines.
1 min read
LangChain Expression Language (LCEL): A Practical Guide
Understanding LangChain Expression Language for building composable LLM applications with clean, declarative syntax.
1 min read
SemPy Library: Python for Semantic Models in Fabric
Deep dive into the SemPy library for working with Power BI semantic models in Microsoft Fabric notebooks.
1 min read
Data Science Workflows in Microsoft Fabric
Building end-to-end data science workflows using Microsoft Fabric's integrated capabilities.
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
LangChain Updates: New Features and Improvements
LangChain's velocity in 2023 has been remarkable and occasionally destabilising — the 0.0.x series has introduced breaking API changes in point releases…
1 min read
PEFT Libraries: Practical Tools for Efficient Fine-Tuning
Tomorrow we'll explore Hugging Face integration with Azure. PEFT Documentation PEFT GitHub TRL Library
1 min read
Building AI Agents: From Chatbots to Autonomous Systems
AI agents represent the next evolution of AI systems. By combining planning, tool use, and memory, they can autonomously accomplish complex goals while…
1 min read
AI Orchestration Patterns: Coordinating Multi-Model Systems
AI orchestration patterns enable building sophisticated AI systems from modular components. The key is designing for reliability, observability, and…
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
Spark ML Patterns for Production Systems
Spark ML provides battle-tested patterns for production machine learning. From feature engineering to model persistence, these patterns ensure reliable ML…
3 min read
AI Assistants in Jupyter and Databricks Notebooks
Error: {type(error).name}: {str(error)} response = openai.ChatCompletion.create( engine="gpt-4", messages=[{"role": "user", "content": prompt}] ) return…
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
LangChain with Azure OpenAI: Getting Started
Combined with Azure OpenAI's enterprise security and compliance, it's a powerful combination.
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
TDD (Test-Driven Development) Overview with Python Example
This cycle is repeated for each new piece of functionality. The result is a suite of tests that provide confidence in the code and make it easier to…
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
Databricks REST API for Advanced Automation
The Databricks REST API exposes every workspace capability that the UI and CLI provide—and then some—making it the integration point for external…
9 min read
Using WSL2 (Windows Subsystem for Linux) for Blockchain Development
In the past, I've been using my Macbook Pro to do all things blockchain and smart contracts development. One of the hurdles I encountered in the past is…
1 min read
PySpark Best Practices for Production Workloads
1. Define schemas explicitly Never rely on schema inference in production 2. Use built in functions Avoid UDFs when possible 3. Write pure transformation…
1 min read
Azure Functions with Python: Getting Started
.NET on Functions is the default story Microsoft tells, but in practice the serverless workload that lands on my desk most often is a Python data scientist…