Prompt Engineering and AI APIs
40 hoursIntermediate8 modules
5 of 8 modules with a published syllabus
0 of 8 modules recorded
Syllabus
Focuses on building on top of third-party models rather than training your own: OpenAI/Anthropic/OSS APIs, function calling and cost; shipping the result as a service with FastAPI and Docker; and evaluating what you built with benchmarks, LLM evals, A/B testing and red teaming. The groundwork comes in through Python, text as data and ethics/bias; the agent orientation comes in through the two introductory Agentic AI modules, which draw the line between a chatbot and an agent.
Modules of this programme
The same objects as the full curriculum. A module opened from any programme that contains it is the same module, with the same syllabus and the same status.
| Code | Module | Content | Hands-on | Status | Price |
|---|---|---|---|---|---|
| CORE-102 | Python for AI | Syntax, functions, environments (venv/conda), notebooks | Mini-project: a simple text analyser | Syllabus published | R$ 120US$ 40MX$ 500estimated price0 of 1 module published |
| CORE-107 | Ethics, bias and safety in AI | Bias in data, privacy, responsible use | Audit an off-the-shelf model for bias | Syllabus published | R$ 120US$ 40MX$ 500estimated price0 of 1 module published |
| AG-101 | What agents are: LLM + tools + loop | Detailed syllabus not written yet | — | Syllabus published | |
| AG-102 | Chatbots vs. agents (hands-on with APIs) | Detailed syllabus not written yet | — | Syllabus published | |
| NLP-101 | Text as data (tokens, cleaning, regex) | Detailed syllabus not written yet | — | Syllabus published | |
| CORE-205 | AI APIs and prompt engineering | OpenAI/Anthropic/OSS, function calling, cost | A CLI app that calls an LLM through an API | Syllabus published | R$ 180US$ 60MX$ 750estimated price0 of 1 module published |
| CORE-206 | Basic deployment | FastAPI, Docker, shipping a model as an API | Put the CORE-203 model into production | Syllabus published | R$ 180US$ 60MX$ 750estimated price0 of 1 module published |
| CORE-304 | Evaluating AI systems | Benchmarks, LLM evals, A/B testing, red teaming | Build an eval suite for an application | Syllabus published | R$ 240US$ 80MX$ 1,000estimated price0 of 1 module published |
- CORE-102Syllabus published
Python for AI
- Content
- Syntax, functions, environments (venv/conda), notebooks
- Hands-on
- Mini-project: a simple text analyser
R$ 120US$ 40MX$ 500estimated price0 of 1 module published
- CORE-107Syllabus published
Ethics, bias and safety in AI
- Content
- Bias in data, privacy, responsible use
- Hands-on
- Audit an off-the-shelf model for bias
R$ 120US$ 40MX$ 500estimated price0 of 1 module published
- AG-101Syllabus published
What agents are: LLM + tools + loop
Detailed syllabus not written yet
- AG-102Syllabus published
Chatbots vs. agents (hands-on with APIs)
Detailed syllabus not written yet
- NLP-101Syllabus published
Text as data (tokens, cleaning, regex)
Detailed syllabus not written yet
- CORE-205Syllabus published
AI APIs and prompt engineering
- Content
- OpenAI/Anthropic/OSS, function calling, cost
- Hands-on
- A CLI app that calls an LLM through an API
R$ 180US$ 60MX$ 750estimated price0 of 1 module published
- CORE-206Syllabus published
Basic deployment
- Content
- FastAPI, Docker, shipping a model as an API
- Hands-on
- Put the CORE-203 model into production
R$ 180US$ 60MX$ 750estimated price0 of 1 module published
- CORE-304Syllabus published
Evaluating AI systems
- Content
- Benchmarks, LLM evals, A/B testing, red teaming
- Hands-on
- Build an eval suite for an application
R$ 240US$ 80MX$ 1,000estimated price0 of 1 module published
Not everything fits into 40 hours. State of the art, distributed training, interpretability, edge deployment and the five integrative projects exist only inside the complete tracks: Tracks