LLM API Integration English: Vocabulary for AI-Powered Applications

Introduction

Integrating large language model APIs into production applications has become a mainstream engineering task. Whether you are building a customer support assistant, a code review tool, or a document analysis pipeline, the vocabulary you use in technical discussions, design documents, and code reviews signals your depth of understanding. This post covers nine essential terms that developers use when working with LLM APIs at a professional level.

LLM API Integration Vocabulary

Prompt engineering — The practice of designing, structuring, and iterating on the text inputs sent to a language model in order to produce more accurate, reliable, and appropriately formatted outputs. Prompt engineering is part craft, part science, and it significantly affects the quality of LLM-powered features.

“We spent two weeks on prompt engineering for the contract analysis feature — the final prompt includes explicit output format instructions, few-shot examples, and a chain-of-thought instruction that reduced hallucination rates by over 60 percent.”

System prompt — An instruction or context block that is provided to a language model before the user’s input, used to define the model’s persona, set behavioral constraints, and provide background information. System prompts are typically invisible to the end user.

“Our system prompt establishes the assistant’s role, defines the output format as structured JSON, and instructs the model to decline any request that falls outside the scope of UK employment law questions.”

Context window — The maximum number of tokens that a language model can process in a single request, including both the input and the output. Content that exceeds the context window must be truncated, summarized, or handled through chunking strategies.

“The document we need to analyze is 180,000 tokens, which exceeds even our largest model’s context window — we are implementing a sliding window chunking strategy with overlap to handle long-form legal documents.”

Temperature — A parameter that controls the randomness or creativity of a model’s output. A temperature of 0 produces highly deterministic, consistent responses, while higher values produce more varied and creative outputs. For factual or structured tasks, low temperature is typically preferred.

“For the customer FAQ bot, we set temperature to 0.1 to ensure consistent, predictable answers — for the creative tagline generator we use 0.9 to encourage more varied and inventive outputs.”

Top-p — Also called nucleus sampling, top-p is a parameter that controls which tokens the model considers at each generation step by limiting the selection to the smallest set of tokens whose cumulative probability reaches the specified value. It is often used alongside temperature to fine-tune output diversity.

“We use a combination of temperature 0.7 and top-p 0.9 for the product description generator — the combination gives us creative variety without producing completely off-topic outputs.”

Hallucination — The tendency of language models to generate plausible-sounding but factually incorrect, unsupported, or fabricated information. Hallucination is one of the primary reliability risks in LLM-powered applications and must be mitigated through grounding, retrieval augmentation, and output validation.

“The legal team flagged that the contract summary tool was hallucinating jurisdiction-specific clauses that did not exist in the source document — we addressed this by switching to a RAG architecture that grounds every claim in retrieved passages.”

RAG — Retrieval-Augmented Generation is an architectural pattern where a language model’s response is grounded in documents retrieved from an external knowledge store rather than relying solely on the model’s parametric memory. RAG significantly reduces hallucination and allows the model to answer questions about proprietary or up-to-date information.

“We implemented a RAG pipeline for the internal support bot: user questions are embedded and used to retrieve the top five relevant knowledge base articles, which are then injected into the prompt as grounding context before the model generates its answer.”

Function calling — A capability provided by some LLM APIs that allows the model to express the intent to call a developer-defined function by returning structured output in a specific format. The developer’s code then executes the function and may return the result to the model for further reasoning.

“We use function calling to let the assistant trigger real-time actions: when a user asks to reschedule a meeting, the model returns a structured call to our calendar API rather than generating a plain text response that we would have to parse.”

Tool use — A broader term for the pattern where an LLM is given access to external capabilities — search engines, code interpreters, APIs, databases — that it can invoke during a generation step. Tool use enables agents to take actions in the world rather than simply producing text.

“The research assistant has four tools available: web search, a PDF reader, a calculator, and a citation lookup API — at each step the model decides which tool to invoke based on what information it still needs to answer the user’s question.”

Key Design Decisions in LLM Integration

When integrating LLM APIs, a few design decisions have outsized impact on quality and reliability. First, invest heavily in system prompt design — a well-crafted system prompt is often worth more than model fine-tuning for production applications. Second, plan for context window limits from the beginning: documents, conversation histories, and retrieved passages all consume tokens, and running out of context is a common production failure mode.

Third, treat hallucination as a first-class engineering concern. For any use case involving factual claims — legal, medical, financial, or product-specific — implement a RAG architecture and validate outputs before they reach users. Finally, evaluate whether function calling or tool use patterns fit your use case: for applications that need to take actions or retrieve real-time data, these patterns are far more reliable than trying to parse intent from free-text model outputs.

Mastering this vocabulary positions you to contribute meaningfully to architectural discussions about AI-powered features, write clearer technical specifications, and debug LLM integration issues with greater precision.

Bridging the Gap: Addressing Nuances for Non-Native Speakers

The integration of Large Language Model (LLM) APIs represents a significant shift in software development, demanding new communication skills. Beyond simply understanding technical concepts like “prompt optimization” or “context window,” developers need to articulate their work clearly and concisely within a professional English environment. This is particularly crucial for those whose first language isn’t English, where subtle differences in phrasing can lead to misunderstandings during code reviews, Slack discussions, or when writing pull request descriptions. The goal isn’t just about translating technical terms; it’s about mastering the style of communication expected within a collaborative software development team.

One common issue arises when discussing the limitations of an LLM’s response. Instead of saying “This doesn’t work,” which can sound accusatory, a more constructive approach is to frame it as, “The model appears to be struggling with nuanced requests regarding [specific aspect]. Perhaps we could refine the prompt to provide clearer examples or adjust the temperature parameter.” Similarly, when requesting changes from another developer during a code review, phrasing like, “Could you investigate whether incorporating this additional context would improve the LLM’s ability to generate accurate results for this scenario?” is far more effective than a blunt “Fix this.” The emphasis here is on collaborative problem-solving and focusing on the model’s behavior rather than assigning blame. Another frequent situation involves describing the intention behind a prompt – “I’m utilizing this prompt to guide the LLM in extracting key entities from unstructured text data, aiming for precision in identifying [specific entity type].”

Furthermore, understanding terminology related to Retrieval-Augmented Generation (RAG) is vital. Instead of simply stating “We’re using RAG,” developers need to explain why they’ve chosen this approach. Phrases like “The RAG architecture allows us to ground the LLM’s responses in our specific knowledge base, mitigating potential hallucinations and improving factual accuracy” are considerably more impactful. It’s about demonstrating a thoughtful understanding of the underlying technology and its benefits. Finally, when documenting API calls or integrating functions, clear and descriptive language is paramount. “This function utilizes the OpenAI completions.create endpoint to generate text based on the provided input prompt, with parameters adjusted for optimal creative output.”

import openai

openai.api_key = "YOUR_API_KEY"  # Replace with your actual API key

response = openai.completions.create(
    model="gpt-3.5-turbo",
    prompt="Write a short poem about a rainy day.",
    max_tokens=50,
    temperature=0.7
)

print(response.choices[0].text)

This simple example demonstrates the kind of API interaction developers will frequently document and discuss – the careful selection of parameters like temperature to influence the model’s output. The key takeaway is that precise language, focusing on intent, impact, and reasoning, elevates communication from simply conveying information to fostering effective collaboration within a complex AI-powered application development workflow.