בינה מלאכותית למפתחים
בנו עם ממשקי LLM, פרומפטים, RAG וסוכנים.
Large language models can now write, summarize, translate, classify and even call your own code, and adding one to an app takes a single HTTP request. This track is for developers who can already program. You learn how models really work (tokens, context windows, next-token prediction and why they can be confidently wrong), call the Anthropic Messages API correctly, write prompts that return reliable JSON, stream answers and keep costs under control. Then you build real features: tool use, retrieval-augmented generation with embeddings, agents with guardrails and MCP, and the safety and testing habits that keep all of it trustworthy.
Every request is shown exactly as it travels over the network.
- שיעורים
- 14
- זמן
- 2 שע׳
- רמה
- ממשיכים
השיעורים במסלול הזה זמינים כרגע באנגלית.
- ארד פתוחה
- כסף פתוחה
- זהב נעולה
מוכנים להוכיח?
שלושה מבחנים מחכים: ארד, כסף וזהב.
מה תדעו לעשות
- Explain tokens, context windows and sampling, and why models can be confidently wrong
- Call the Anthropic Messages API safely from a server, with streaming
- Write prompts that return JSON, and validate it before you trust it
- Control cost and limits with token budgets, caching and retries
- Build tool use, RAG and a small agent with guardrails
- Defend against prompt injection and measure quality with evals
המסע
1 פרק 1Working with models
How LLMs work, your first API call, prompting, streaming and costs.
0 / 52 פרק 2Building real features
Tool use, retrieval, agents, safety and evaluation.
0 / 53 פרק 3Next level
Structured outputs, images and documents, deeper evals and your own MCP server.
0 / 4