Building on Top of LLM APIs
This blog introduces developers to building on top of Large Language Model APIs, showing how to treat LLMs as a new backend capability for text understanding, generation, and transformation. It covers common product patterns (assistants, content tools, extraction, and RAG), key design principles for prompts, context, and validation, and a simple starter architecture for adding LLM-powered features to real-world applications.
3/6/20232 min read
At a high level, an LLM API is just a function:
Input: instructions + context (the prompt)
Output: structured or unstructured text (answer, code, JSON, summary, etc.)
You don’t train the model; you compose around it. Your job is to:
Tell it who it is (e.g., “You are a helpful support assistant for product X…”).
Tell it what to do (e.g., “Answer the user’s question using the documentation below…”).
Tell it how to respond (e.g., “Return JSON with fields: answer, confidence, suggested_links.”).
Once you see it this way, an LLM is just another microservice—one that happens to be very good at language.
Common Product Patterns You Can Build
Here are some realistic patterns you can ship quickly:
Chat-style Assistants
Customer support bots grounded in your FAQ and docs
Internal assistants for HR/IT over company policies
Dev-doc bots that answer questions from your API reference
Content and Communication Helpers
Drafting emails, release notes, product descriptions
Summarizing tickets, calls, or long reports
Rewriting text for different tones or audiences
Extraction and Classification Pipelines
Turn messy text into structured JSON (entities, labels, categories)
Auto-tag incoming tickets, leads, or documents
Classify sentiment, urgency, or topic
RAG: Search + LLM
Search your own data (Elasticsearch, Postgres, vector DB)
Feed the most relevant chunks into the prompt
Let the LLM generate grounded answers using that context
Each of these is “just” normal app logic + an LLM call at the right moment.
Designing Around an LLM API
To use an LLM effectively, a few principles help:
1. Treat Prompts Like Code
Prompts are logic. Version them, keep examples, and don’t be afraid to A/B test different prompt styles. Small changes (“step-by-step,” “respond in JSON”) can drastically improve reliability.
2. Control the Context
Models don’t automatically “see” your data. You must decide:
What to fetch (top search hits, last N messages, user profile)
How much to include (token limits are real)
How to format it (clear section labels, delimiters, etc.)
Good context plumbing is half the battle.
3. Validate the Output
Never assume the model returned exactly what you asked for.
Ask for strict formats (JSON, markdown sections).
Parse and validate before trusting.
Add fallbacks: if parsing fails, fall back to a generic answer or a human review path.
4. Mind Cost and Latency
Every call spends tokens and time. Cache where possible, choose smaller models for simple tasks, and design UX that tolerates AI response times.
A Simple Starter Architecture
A basic LLM-powered feature often looks like this:
Frontend – chat box or form.
Backend – receives the request, fetches relevant data, builds a prompt, calls the LLM API.
Post-processing – validate, extract structured fields, and return a clean response (or trigger actions).
From there you can layer on: authentication, logging, analytics, prompt versioning, and human-in-the-loop review.
LLM APIs don’t require you to become an ML researcher. They invite you to think of “language understanding and generation” as just another service in your architecture. Once you start treating them like a backend building block—alongside databases, queues, and payments—you unlock a new class of features that used to be out of reach for most teams.
