Technical insights
Deep dives into AI development, machine learning techniques, and practical implementation guides.
2025-12-15
From Chatbots to Agents: When AI Starts Doing the Work
Chatbots answer questions. Agents use tools to execute workflows. Learn the operating model, safeguards, and architecture needed to ship agentic AI safely.
Read article2025-12-15
Beyond "Vibe Checking": How to Evaluate AI Systems at Scale
Most teams judge LLM output by feel. Learn how to run evaluations (evals) like tests, use LLM-as-a-judge safely, and prevent regressions when models and prompts change.
Read article2025-12-15
Fine-Tuning vs. RAG: Choosing the Right Architecture
Should you fine-tune a model on your data or connect it to your data with RAG? Learn the trade-offs, costs, and the architectures we see succeed in production.
Read article2025-12-15
The Last Mile Problem: Why AI Coding Agents Don’t Ship Software
AI coding agents generate code fast, but shipping the right feature still depends on scope alignment, evidence, and verification. Learn how to reduce agent drift with scope-first development.
Read article2025-12-15
The Multi-Model Strategy: Why You Shouldn't Lock In to OpenAI
Learn why multi-model routing reduces cost, improves reliability, and supports data privacy. A practical guide to building a router architecture for enterprise AI.
Read article2024-12-15
Understanding Prompt Caching: Reduce AI Costs and Latency
Learn how prompt caching works in modern LLM APIs like Anthropic Claude and OpenAI. Understand cache prefixes, TTL, cost savings, and when to use prompt caching for enterprise AI applications.
Read article2024-12-14
Why Embeddings Matter: A Technical Guide for Business Leaders
Understand embeddings from vectors to applications. Learn how embedding models like OpenAI, Cohere, and sentence-transformers enable semantic search, recommendations, and RAG systems.
Read article2024-12-13
Writing Effective Prompts for LLMs
Master prompt engineering with chain-of-thought, system prompts, few-shot learning, and structured outputs. Practical techniques for Claude, GPT-4, and other modern LLMs.
Read article2024-12-12
Understanding Generative Search and Its Application in Your Company
Learn how generative search (RAG) combines vector databases like Pinecone with LLMs to provide accurate, up-to-date responses. Implementation strategies and business applications.
Read article2024-12-11
Demystifying Transformer Models in Machine Learning
Understand transformer models - the architecture powering modern AI. Explore tokenization, embeddings, attention mechanisms, and why this matters for your business AI strategy.
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