Plan‑Act‑Reflect: A Simple Agent Loop That Works
Plan‑Act‑Reflect: a practical loop for continuous improvement Use the Plan‑Act‑Reflect loop to run focused experiments, learn faster, and improve outcomes
Automatic vs. Human Evaluation: When Each Shines
Choosing Human vs. Automatic Evaluation for AI Outputs Learn when to use human, automatic, or hybrid evaluation for AI outputs to reduce risk and improve q
GPU vs. CPU vs. NPU: What Matters for Local Models
Choosing Hardware for On-Device Inference: GPU, CPU, or NPU? Decide the right on-device inference hardware to meet latency, throughput, and power goals — p
Choosing a Vector DB: Lite vs. Heavyweight Options
Choosing the Right Database for High-Throughput Applications Compare performance, cost, and integration trade-offs to pick a database that meets throughput
How to Write Task‑Specific Prompts for Reliable Output
How to Write Effective Prompts for Reliable AI Output Learn a structured prompt framework to get accurate, usable AI results—clear outputs, validation step
Tokens, Context Windows, and Why Your Prompt Gets Cut Off
How to Keep Prompts Within an LLM’s Context Window Prevent cut-off prompts, fit crucial info into the context window, and get consistent outputs — practica
APIs vs. SDKs vs. Frameworks: Choosing Your Path
Choosing Between APIs, SDKs, and Frameworks for Your Project Pick the right integration approach to speed development, reduce risk, and deliver reliable ap
Data Retention Policies for AI Apps (Copy‑Ready)
Data Retention Policy for AI Applications Create a clear, enforceable data-retention policy for AI: limit scope, automate deletion or anonymization, keep a
Entity‑First Content: Build Topic Authority the Right Way
Entity-First SEO: Build Search Authority with Structured Knowledge Develop an entity-first SEO approach to improve discoverability, match user intent, and
Collect, Clean, Consent: Ethical Data Sourcing for AI
Building High-Quality, Compliant Data Pipelines for Machine Learning Design ML-ready data pipelines that meet goals, preserve privacy, and ensure quality —