Traineeship

Prompt Engineer AI & Data

KEY SKILLS: SQL, JSON, CSV, RAG architectures and vector stores (such as FAISS, Pinecone, LLMs and prompt design (ideally OpenAI or similar), Python

4.7 (253 user ratings)

Rated 4.7 out of 5

What You’ll Learn:

You’ll learn how to design, test and refine prompts, clean and map unstructured text and structured fields into usable metadata and semantic indexes for use in RAG systems and collaborate with product and design teams to embed LLM-powered assistants into customer-facing support tools

At the end of this course, you will know how to;

Work across LLM design, structured data integration
Create prompt libraries
Build pipelines to structure documents, notes, data, and multimedia into formats consumable by LLMs/AI agents
Support the delivery of features like in-context help, AI-powered insights, conversation-based documentation, and lookup assistants
Build and run prompt evaluation frameworks and create feedback loops
Test and evaluate LLM output (for correctness, adverse results, etc., with efficient and reusable processes)
Prompt engineering (iterating on the structure, semantics, and content of the data sent to the LLM for processing)
Data pre/post-processing (storage, ingestion, and manipulation of data sent to and received from the LLM)