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LLMs

Large language models, prompting, tool use, and agentic patterns.

Browse 4 tutorials → Other topics

Why learn LLMs?

  • The fastest-growing capability in software since the smartphone.

  • Used in code review, search, support, content, and agentic workflows.

  • Every product team is now adding AI features.

  • A skill that pairs well with any backend or frontend role.

What you can build with LLMs

Chatbots and copilots Document Q&A and search Workflow automation with tool use Content generation and summarization

LLMs tutorials

4 articles

Hand-written tutorials, ordered as a recommended learning path.

  1. 01 Prompt Engineering Basics A practical guide to prompting LLMs — system vs user prompts, clarity over cleverness, few-shot examples, structured JSON output, chain-of-thought, and eval-driven iteration.
  2. 02 Tool Use & Function Calling How LLMs call functions: defining tools with JSON schema, the request → tool-call → response loop, common patterns like search and database queries, and the failure modes that bite in production.
  3. 03 LLM Evaluation Why vibes do not scale: building golden datasets, exact-match vs LLM-as-judge scoring, A/B comparing prompts and models, regression suites, and the observability you need to ship safely.
  4. 04 Templates & Patterns How to turn prompts into reusable templates: variables, system messages as contracts, few-shot structure, JSON output schemas, role prompting, and when patterns help versus hurt.