All articles
From concepts to working code.
What Is an LLM? A Beginner’s Guide from Tokens to Context
Start with why a model can answer questions, then understand its capabilities and limits.
Read article →Selection methodHow to Choose an LLM: Task, Quality, Speed, and Budget
Do not rely on leaderboards alone: design a repeatable small test for your own work.
Read article →Practical tipsHow to Write Better Prompts: A Four-Step Template with Examples
Use a goal, background, constraints, and acceptance checks so the model can understand the request.
Read article →Developer practiceBefore Calling an LLM API: Estimate Costs and Protect Your Key
Understand request volume, billing dimensions, and credential storage before a test becomes an unexpected bill.
Read article →Model mapHow to Understand Major LLM Families: GPT, Claude, Gemini, DeepSeek, and Qwen
Separate providers, model families, and chat products; a name is not a guarantee of capability.
Read article →Developer practiceYour First LLM API Call: Environment Variables, Requests, and Errors
Use Python’s standard library to understand one request without putting a key in a webpage.
Read article →Knowledge baseWhat Is RAG? Let a Model Answer from Your Documents
Understand retrieval-augmented generation, citations, and its failure boundaries.
Read article →Selection methodHow to Compare Models Reliably: A Scorecard and Experiment Plan
Do not draw conclusions from one answer; compare models with consistent inputs and records.
Read article →Beginner guideLocal Deployment or Cloud API? Choose by Privacy, Hardware, and Maintenance
Compare responsibilities on both routes rather than asking only whether one is free.
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