BLOG · 6 ARTICLES
Guides for getting the most out of AI models
Methods, not rankings. Prices and benchmarks change constantly, so these guides teach you how to evaluate and calculate — any numbers used are clearly labeled hypothetical examples.
- prompting
Prompt Writing Techniques That Actually Help (With Examples)
Practical prompt engineering techniques that improve LLM output: clear task framing, context, examples, structure, output formats, step-by-step reasoning and testing.
- evaluation
How to Evaluate LLMs on Your Own Tasks: Build a Small Eval Set
Leaderboards can't tell you how a model handles your data. Learn to build a 30–100 example eval set, choose scoring methods, and compare models fairly.
- cost
How to Cut LLM API Costs: 8 Levers That Actually Work
Reduce LLM API spend with shorter prompts, output caps, prompt caching, batch processing, smaller models and routing — plus how to measure the savings before you ship.
- context window
Context Window Explained: What It Means in Practice
What an LLM context window is, what counts against it, why bigger isn't automatically better, and practical ways to budget and manage context in real applications.
- pricing
What Are Tokens? How to Estimate LLM API Cost (Worked Example)
Tokens explained in plain English, the formula providers use to bill LLM API calls, and a step-by-step cost estimate using clearly hypothetical prices.
- model selection
How to Choose an LLM for Your Use Case: A Criteria Checklist
A practical checklist for picking a large language model: task fit, quality on your data, context window, latency, cost, deployment, data handling and lock-in risk.