1 min read
RAG Chunking Strategies: Finding the Optimal Approach
The simplest approach splits text at regular intervals with optional overlap. Semantic chunking respects content boundaries like paragraphs and sections.
NLP
39 articles
1 min read
Chunking Strategies for RAG: Finding the Right Granularity
Choose chunking strategy based on document structure and retrieval requirements.
1 min read
Audio AI Updates 2025: Speech Recognition and Synthesis Advances
Audio AI enables natural voice interactions and automated content processing. Choose the right tool for your latency and accuracy requirements.
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
Semantic Router: Intent-Based LLM Routing
Semantic routing compares the meaning of a user's query against a set of example utterances. When the query is semantically similar to examples for a…
1 min read
Advanced Chunking Strategies for RAG: Beyond Fixed-Size Splits
In projects I've worked on, chunking decisions alone changed retrieval quality more than model choice ever did. This deep dive pulls together advanced…
1 min read
Text Summarization API in Azure AI Language
Learn how to use Azure AI Language's summarization capabilities for extractive and abstractive document summarization.
2 min read
Custom Named Entity Recognition with Azure AI Language
Build custom NER models to extract domain-specific entities from text using Azure AI Language.
1 min read
Custom Text Classification with Azure AI Language
Build custom text classification models using Azure AI Language for domain-specific categorization needs.
1 min read
Azure AI Language Updates: New NLP Capabilities
Explore the latest updates to Azure AI Language including improved entity recognition, sentiment analysis, and text analytics capabilities.
1 min read
Speech-to-Text Improvements in Azure AI: Accuracy and Performance
Deep dive into the latest speech-to-text improvements in Azure AI including better accuracy, noise handling, and domain-specific recognition.
1 min read
Semantic Compression: Preserving Meaning with Fewer Tokens
Tomorrow we'll explore context caching strategies. Text Summarization Survey Sentence Transformers ROUGE Score
1 min read
Transformers Library: The Swiss Army Knife of NLP
Tomorrow we'll explore the Accelerate library for distributed training. Transformers Documentation Pipeline API Model Hub
1 min read
Entity Extraction at Scale with LLMs
Entity extraction at scale transforms unstructured text into structured knowledge. Combining LLM intelligence with distributed processing enables insights…
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
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
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
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…
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
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…
1 min read
Building Skillsets in Azure Cognitive Search
Skillsets enable powerful AI-driven content enrichment during the indexing process.
1 min read
Semantic Search in Azure Cognitive Search
Semantic search dramatically improves search relevance by understanding user intent rather than just matching keywords.
1 min read
Key Phrase Extraction with AI Builder: Identifying Important Concepts
Key phrase extraction enables: Automatic document tagging Quick content summarization Topic trend identification Enhanced search and discovery It's a simple…
1 min read
Entity Extraction with AI Builder: Mining Structured Data from Text
Train models to extract domain specific entities: Entity extraction turns unstructured text into structured data: Automatically identify people,…
1 min read
Category Classification with AI Builder: Automating Text Categorization
For content that belongs to multiple categories: Complete implementation: Category classification automates text organization: Route content to appropriate…
1 min read
Sentiment Analysis in Power Platform: Understanding Customer Emotions
AI Builder's sentiment analysis capability classifies text into positive, negative, neutral, or mixed sentiment categories—the prebuilt model runs over…
1 min read
Named Entity Recognition with Azure Cognitive Services
Named Entity Recognition is essential for extracting structured information from unstructured text, enabling applications from search enhancement to…
1 min read
Extracting Key Phrases from Text with Azure Cognitive Services
Key phrase extraction is a foundational NLP capability that enables efficient processing of large text collections and powers intelligent content management…
1 min read
Building Sentiment Analysis Solutions with Azure
Sentiment analysis transforms unstructured feedback into actionable insights, enabling data-driven decisions about products, services, and customer experience.
1 min read
Text Analytics with Azure Cognitive Services
Text Analytics enables rich understanding of unstructured text, powering applications from customer feedback analysis to content recommendation systems.
1 min read
Data Labeling Projects in Azure Machine Learning
Data labeling in Azure ML streamlines the process of creating high-quality training data, essential for building accurate machine learning models.
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"…
2 min read
Azure Cognitive Services: Building Intelligent Applications
Cognitive Services in mid-2021 is a sprawling catalogue—vision, speech, language, decision—that can be disorienting to navigate. Today's post is the…
1 min read
Multilingual Applications with Azure Translator
Azure Translator is one of the Cognitive Services that I've shipped most quietly—it tends to be a two-day integration rather than a headline feature, but…
1 min read
Natural Language Understanding with LUIS
Intents : Categories of user actions (e.g., BookFlight, GetWeather) Entities : Important data to extract (e.g., locations, dates, quantities) Utterances :…
1 min read
Building FAQ Bots with QnA Maker
QnA Maker simplifies building knowledge based chatbots: Easy content import from URLs, documents, and manual entry Natural language understanding for…
1 min read
Building Intelligent Bots with Azure Bot Service
I've built bot projects with the Bot Framework that I'm proud of, and bot projects that I wish I could forget. The difference is almost always scope…
1 min read
Azure LUIS: Language Understanding for Bots
LUIS: teaching machines to understand human language.
2 min read
Analyzing Text with Azure Cognitive Services Text Analytics
A retail client this week handed me six months of customer feedback in a CSV and asked the question I get every couple of months: "what are people actually…