Adding AI to a product rarely means training your own model. For the overwhelming majority of products, it means calling an existing large language model (LLM) and feeding it the right context — your data, your documents, your users' content — so it produces useful, grounded answers. The hard part isn't the model. It's the architecture around it.
This guide covers the practical decisions: when you need retrieval-augmented generation (RAG), how to choose and use a vector database, and the integration patterns that keep AI features accurate, fast, and affordable.
Start With the Simplest Thing That Works
Before reaching for vector databases and retrieval pipelines, ask whether you need them at all. There's a ladder of complexity, and you should climb only as high as your problem requires:
- Direct prompting: Just call the model with a well-crafted prompt. Perfect for summarization, rewriting, classification, and generation tasks that don't depend on your private data.
- Prompt with injected context: Pass relevant data directly in the prompt (the user's profile, the current document, a small reference table). Works whenever the relevant context is small enough to fit in the context window.
- Retrieval-augmented generation (RAG): When the relevant knowledge is too large to fit in a prompt — a whole documentation site, thousands of support tickets, a user's entire file library — you retrieve only the most relevant pieces at query time and inject those. This is where vector databases come in.
- Fine-tuning: Adjusting a model's weights on your data. Useful for teaching a consistent style or format, but it does not teach the model new facts reliably. Most teams never need it.
A huge number of "we need RAG" projects are actually solved by step 2. Don't build a retrieval pipeline for data that fits in a prompt.
How RAG Actually Works
Retrieval-augmented generation has two phases.
Indexing (done ahead of time):
- Break your source content into chunks (paragraphs or sections).
- Convert each chunk into an embedding — a numerical vector that captures its meaning — using an embedding model.
- Store those vectors, along with the original text, in a vector database.
Querying (done per request):
- Convert the user's question into an embedding with the same model.
- Search the vector database for the chunks whose embeddings are most similar to the question.
- Inject those top chunks into the prompt as context, and ask the model to answer using them.
The result: the model answers from your knowledge, with citations you can show the user, instead of relying on what it happened to memorize during training. This dramatically reduces hallucination and keeps answers current as your data changes.
Choosing a Vector Database
You have three broad options, and the right one depends on scale and how much infrastructure you want to run.
- Postgres with pgvector: If you already use PostgreSQL, the pgvector extension lets you store and search embeddings right alongside your relational data. For most products — up to millions of vectors — this is the best default. One database, no extra service, transactional consistency with the rest of your data.
- Managed vector databases (Pinecone, Weaviate, Qdrant Cloud): Purpose-built for vector search at large scale, with features like metadata filtering, hybrid search, and high-throughput indexing. Reach for these when you outgrow pgvector or need their specific capabilities.
- Embedded/local libraries (FAISS, Chroma): Great for prototypes, notebooks, and small datasets that fit in memory. Less suited to production multi-user apps.
The honest recommendation for most teams: start with pgvector. Adding a separate vector database is a real operational cost, and you can migrate later if scale demands it. (If you're deciding on your primary database too, the SQL vs NoSQL Selector walks through that choice.)
Architecture Patterns That Keep AI Features Sane
AI features have failure modes ordinary features don't. A few patterns prevent most of the pain:
- Stream responses. LLM calls are slow — often several seconds. Stream tokens to the UI as they're generated so the app feels responsive instead of frozen.
- Cache aggressively. Identical or near-identical prompts are common. Caching responses (and embeddings) cuts both latency and cost, which is significant given that AI calls are usually the most expensive part of the request.
- Set timeouts and fallbacks. Model APIs have outages and latency spikes. Decide what happens when a call fails or times out — a graceful error beats a hung request.
- Keep humans in the loop for high-stakes output. If the AI drafts something consequential (an email, a contract clause, a financial figure), let a person review before it's sent or saved.
- Log prompts and outputs. You can't debug or improve what you can't see. Store inputs and outputs (mindful of privacy) so you can diagnose bad answers and measure quality over time.
Cost and Latency Are Architectural Concerns
Unlike a normal database query, every AI call costs real money and takes real time. Treat both as first-class design constraints:
- Choose the smallest model that does the job. Use a fast, cheap model for simple tasks (classification, routing, short answers) and reserve the largest models for genuinely hard reasoning. Mixing models by task is one of the biggest cost levers you have.
- Control the context size. RAG that stuffs twenty chunks into every prompt is slow and expensive. Retrieve fewer, better chunks.
- Monitor spend from day one. AI costs scale with usage in a way that can surprise you. Track cost per request and set alerts.
Putting It Together
For most products, adding AI means: call an existing model, give it the right context, and build careful plumbing around the call. Climb the complexity ladder only as far as you must — direct prompting first, RAG with pgvector when your knowledge base is too big to inline, and a dedicated vector database only once scale demands it. Stream responses, cache, log, and watch costs.
If you're planning AI features into a product, Blueprint AI generates a complete technical specification — including the data model, the AI integration architecture, and a realistic cost estimate — from a description of what you want to build. The Software Development Cost Estimator can help you budget the build itself.
