A Beginner's Guide to Building Large Language Model Applications: Prompt Engineering, Retrieval‑Augmented Generation, Function Calling, and AI Agents

Large language models (LLMs) have rapidly become central to modern software development, yet many developers feel anxious about being replaced; the article argues that instead of fearing the technology, developers should embrace it by learning how to integrate LLMs into real business workflows.

Prompt Engineering is presented as the first step: by crafting clear, structured prompts—using zero‑shot or few‑shot examples—developers can guide LLMs to produce deterministic JSON outputs, extract subject‑predicate‑object triples, or perform multi‑turn interactions.

Typical LLM applications start with a system prompt that defines the task, followed by user queries. The article shows example code snippets such as:

func AddScore(uid string, score int) { // first interaction
    user := userService.GetUserInfo(uid)
    newScore := user.score + score
    // second interaction
    userService.UpdateScore(uid, score)
}

To handle more complex workflows, Function Calling allows the model to request external tools. An example OpenAPI‑style definition is shown:

service SearchLLM {
  rpc GetSearchKeywords(Question) returns Keywords;
  rpc Summarize(QuestionAndSearchResult) returns Answer;
}

When a model needs additional data, it can return a tool_calls array, e.g.:

{"tool_calls":[{"id":"call_id_1","type":"function","function":{"name":"get_weather","arguments":"{\"city\":\"Beijing\",\"date\":\"2025-02-27\"}"}}]}

The article then introduces Retrieval‑Augmented Generation (RAG) as a solution to LLM context length limits. By chunking documents, embedding each chunk into high‑dimensional vectors, and storing them in a Vector Database , relevant passages can be retrieved via similarity search and injected into the prompt:

SELECT * FROM docs ORDER BY cosineDistance(vector, [0.1,0.2,…]) LIMIT 5;

Effective RAG depends on good Chunking (preserving context while keeping chunks small) and high‑quality Embedding models, which may be open‑source, fine‑tuned, or provided by LLM vendors.

For code‑related use cases, the same principles apply but require code‑specific chunking and embedding. The article discusses how tools like Cursor or GitHub Copilot embed code snippets, retrieve relevant code via vector search, and combine them with prompts to generate accurate completions.

Beyond question‑answering, the article explores building AI Agents that can execute multi‑step tasks using a suite of tools. It introduces the Modal Context Protocol (MCP) , which defines a client‑server interaction model where an mcp‑client (the LLM application) discovers available tools from one or more mcp‑server instances (local or remote) and orchestrates them to fulfill complex user requests.

Finally, the article summarizes three practical directions for developers: (1) experimenting with LLM‑centric frameworks, (2) focusing on RAG pipelines (chunking, embedding, vector search) to create product‑level knowledge bases, and (3) building MCP‑servers to expose useful tools, enabling AI agents that truly augment daily work.