Building with LLMs beyond the chat window

Using a language model through a chat interface and building a product that uses that same model are very different experiences. The second requires understanding how to steer it, ground it in real information, and integrate it into a larger system.

Prompt engineering: steering the model's behavior

A large language model doesn't "know" what you want unless you tell it precisely. Prompt engineering is the craft of structuring instructions, examples, and context to get consistent, useful outputs — not just plausible-sounding answers.

The limits of the model's knowledge

A model trained up to a certain date knows nothing about what happened afterward, and has no access to your company's private data. That's where RAG (Retrieval-Augmented Generation) comes in: searching for relevant information in an external source and feeding it to the model as context before it answers.

How RAG works in practice

1. The question's text is turned into an embedding (a numeric vector).
2. The closest-matching document embeddings are found in a vector database.
3. Those documents are included as context in the prompt sent to the model.
4. The model generates a response grounded in real information, not just what it memorized during training.

context = find_relevant_documents(question, top_k=3)
prompt = f"Context:\n{context}\n\nQuestion: {question}"
answer = model.generate(prompt)

Why this isn't "just calling the API"

Building a reliable RAG system involves deciding how to chunk documents, which embedding model to use, how to evaluate whether answers are correct, and how to handle cases where the model finds no relevant information — real engineering decisions, not trivial configuration.

Start building

Explore the Generative AI path to learn how to build these systems step by step, from the fundamentals to a full RAG pipeline.