AI Engineering Internship

Send your first prompt to a real language model and read its reply.

Model output is text — normalize it before you trust it.

Ask for structured output and parse it in code.

Use a system message to fix the model's role and constraints.

Show the model labeled examples so it copies the pattern.

Constrain the shape of the output, not just the content.

Ask for JSON and parse it into a dict.

Force the model to pick exactly one label from a set.

Compress text while keeping the meaning.

Transform text into another language or tone.

Feed one model output into the next prompt.

Validate model output and retry until it parses.

Turn text into a vector of numbers.

Compare two embeddings with cosine similarity.

Find the most relevant document for a query by meaning.

Split long text into overlapping pieces for retrieval.

Find the chunk that answers a question.

Retrieve context, then answer grounded in it.

Use the model to route — does this need a tool?

Turn a question into a tool call and run it.

Ask the model to reason, then extract the final answer.

Use a model to evaluate model output.

Keep the model on-topic and safe with a system prompt.

Estimate tokens and cost before you ship.

Classify a batch of support tickets.

A retrieval bot that answers from a FAQ.

Generate content that obeys hard constraints.

Extract structured fields from messy text.

Measure accuracy against a labeled test set.

Wrap everything into one reusable assistant function.