LLM Engineering Fundamentals

What You Will Learn

LLM engineering is the practice of building reliable applications around large language models. This guide covers prompting, tokens, embeddings, retrieval, evaluation, and production guardrails.

Prerequisites

• Basic API knowledge
• Basic application architecture
• Curiosity about AI systems

Concept Overview

An LLM predicts text based on context. Applications become useful when they add the right instructions, private context, tools, memory, and validation around the model.

Step-by-Step Explanation

1. Learn prompts, system instructions, and examples.
2. Understand tokens and context windows.
3. Use embeddings to represent text for similarity search.
4. Build RAG when the model needs private or fresh knowledge.
5. Add evaluations for expected behavior.
6. Add guardrails for safety, format, privacy, and permissions.
7. Monitor cost, latency, and quality.

Code Example

type SupportSummary = {
  issue: string;
  priority: "low" | "medium" | "high";
  nextAction: string;
};

async function summarizeTicket(ticketText: string): Promise<SupportSummary> {
  const response = await llm.generateJson({
    instructions: "Summarize the support ticket into the required JSON shape.",
    input: ticketText,
    schemaName: "SupportSummary",
  });

  return response as SupportSummary;
}

Real-World Use Cases

• Search over company documents
• Customer support drafts
• Code assistants
• Summarization pipelines
• Data extraction workflows

Best Practices

• Give the model clear task boundaries.
• Provide only relevant context.
• Use structured outputs for downstream processing.
• Evaluate with a representative dataset.
• Monitor token usage and latency.
• Keep sensitive data handling explicit.

Common Mistakes

• Expecting the model to know private data
• Adding too much context
• Skipping output validation
• Using manual testing only
• Ignoring security when tools are involved

Interview Questions

• What is a token?
• What are embeddings?
• What is RAG?
• Why do LLM applications need evaluations?
• What are common LLM safety risks?

Summary

LLM engineering is less about one perfect prompt and more about system design. Good context, evaluation, validation, and guardrails make AI features reliable.