Blog
Latest articles
6 min readOpenAI's o1 and o3 Reasoning Models Explained: When to Use Them vs GPT-4o
What makes o1 and o3 different from GPT-4o, where they dramatically outperform standard models, where they are overkill, and how to decide which model to use.
9 min readUsing LLMs for Business Analysis and Decision Support: What Works, What Doesn't
Where LLMs genuinely add value in business analysis, where they are unreliable, and the analyst workflow that gets results without creating liability.
6 min readLLM Privacy for Enterprise: What Actually Happens to Your Data
Whether providers train on your API calls, what zero data retention means, on-premise alternatives, and how to build a practical LLM data policy for your team.
9 min readRunning LLMs Locally for Privacy-Sensitive Work: A Practical Setup Guide
When local LLMs make sense for privacy, how to set up Ollama with IDE integration, performance benchmarks on real hardware, and the honest quality trade-off.
10 min readVercel AI SDK: The Complete Guide for Building AI-Powered Next.js Apps
How the Vercel AI SDK unifies LLM providers, the key functions (streamText, generateObject, useChat), Next.js App Router integration, and when to use it over calling APIs directly.
10 min readAnthropic API Guide: Claude Integration From Authentication to Prompt Caching
Complete guide to the Anthropic API - authentication, message format, streaming, tool use, prompt caching for 90% cost reduction, batch processing, and production error handling.
6 min readOpenAI API Guide 2026: Models, Structured Outputs, Batch API, and Cost Optimization
Complete OpenAI API reference for 2026 - model catalog, chat completions, function calling, structured outputs with JSON schema, embeddings, rate limits, and cost management.
7 min readLLM Context Window Sizes Compared in 2026: What Fits, What Doesn't, and the Lost-in-the-Middle Problem
Context windows from 128k to 1M tokens compared - what fits in each size, the lost-in-the-middle accuracy problem, and practical guidance for choosing the right model for your context needs.
8 min readPrompting for Summarization: Techniques That Actually Work
Learn how to write prompts that produce accurate, concise summaries - covering length control, chain-of-density compression, source citation, and prompt differences across email, meeting, and document contexts.
8 min readPrompting for Classification: Getting Consistent Labels Every Time
How to write prompts that produce reliable, consistent classification labels - covering category definitions, JSON output, multi-label vs single-label, confidence scores, and when to use zero-shot vs few-shot vs fine-tuning.
6 min readPrompting for Data Extraction: Structured Output From Unstructured Text
A complete guide to extracting structured data from text with LLMs - field definitions, JSON schemas, function calling for guaranteed structure, missing field handling, batch efficiency, and accuracy limits.
9 min readPrompting for Creative Writing: Getting Less Generic Output
How to escape generic AI writing - using specific stylistic constraints, exclusion lists, tone references, temperature settings, and iterative generate-critique-revise loops to get genuinely distinctive creative output.