Which vector database should I use for RAG?

For production at scale, Pinecone, Weaviate, and Qdrant are popular choices. For prototyping and small datasets, Chroma (local) or pgvector (PostgreSQL extension) are simpler to set up. The choice matters less than chunk quality and retrieval strategy.

How do I handle documents that change frequently?

Implement an incremental update pipeline: detect changed documents, re-chunk and re-embed them, delete the old chunks from the vector DB by document ID, and insert the new chunks. Most vector databases support document-level deletion by metadata filter.

What is the biggest mistake teams make building their first RAG system?

Skipping retrieval evaluation. Teams build the pipeline, run a few manual tests, and ship. In production, retrieval fails on 20-40% of queries because the chunks are too large, the embedding model doesn't match the query vocabulary, or relevant context is split across chunk boundaries. Measure Retrieval Recall@5 before shipping.

Which vector database should I use for RAG?

For production at scale, Pinecone, Weaviate, and Qdrant are popular choices. For prototyping and small datasets, Chroma (local) or pgvector (PostgreSQL extension) are simpler to set up. The choice matters less than chunk quality and retrieval strategy.

How do I handle documents that change frequently?

Implement an incremental update pipeline: detect changed documents, re-chunk and re-embed them, delete the old chunks from the vector DB by document ID, and insert the new chunks. Most vector databases support document-level deletion by metadata filter.

What is the biggest mistake teams make building their first RAG system?

Skipping retrieval evaluation. Teams build the pipeline, run a few manual tests, and ship. In production, retrieval fails on 20-40% of queries because the chunks are too large, the embedding model doesn't match the query vocabulary, or relevant context is split across chunk boundaries. Measure Retrieval Recall@5 before shipping.

Building RAG Pipelines with AI

The Problem

LLMs have a training cutoff and cannot access your proprietary documents, internal knowledge bases, or real-time data. Building a chatbot or Q&A system that answers questions about your company's policies, product documentation, or codebase requires augmenting the model with your specific knowledge — which is exactly what RAG achieves.

How AI Helps

01.Generates embedding-ready text chunks from raw documents using AI-powered segmentation that respects semantic boundaries rather than arbitrary character counts.
02.Writes the query-time retrieval logic: embedding the user question, searching the vector database for relevant chunks, and formatting the retrieved context for injection into the LLM prompt.
03.Evaluates retrieval quality by asking the LLM to assess whether each retrieved chunk is relevant to the query — enabling automatic quality monitoring of the RAG pipeline.
04.Synthesises multi-document answers by retrieving from multiple knowledge sources simultaneously and combining the results coherently, with citations to source documents.
05.Assists with the grounding prompt — the system prompt that instructs the model to answer only from the provided context and to say "I don't know" when the answer is not in the retrieved documents.

Recommended Tools

ACO

AI Chunk Overlap Tool

Split text into token-sized chunks with configurable overlap for RAG and embedding pipelines.

AIP

AI Input Preprocessor

Full preprocessing pipeline for LLM input: trim, normalize, strip HTML, collapse whitespace, and truncate to context window.

VAL

AI Structured Output Validator

Validate AI JSON output against a JSON Schema — check types, required fields, enums.

PLV

AI Pipeline Visualizer

Visualize AI prompt chain JSON as a vertical flowchart.

CHN

AI Prompt Chain Builder

Design multi-step AI prompt chains with variable references between steps.

Recommended Models

gpt-4oclaude-3-5-sonnet-20241022gpt-4o-mini

Example Prompts

[prompt]

Data Analysis Prompt

Most AI data analysis prompts produce vague observations like "sales increased in Q2". This prompt f...

[prompt]

Code Documentation Prompt

Auto-generated documentation is only useful when it goes beyond repeating the function signature. Th...

FAQ

Which vector database should I use for RAG?: For production at scale, Pinecone, Weaviate, and Qdrant are popular choices. For prototyping and small datasets, Chroma (local) or pgvector (PostgreSQL extension) are simpler to set up. The choice matters less than chunk quality and retrieval strategy.
How do I handle documents that change frequently?: Implement an incremental update pipeline: detect changed documents, re-chunk and re-embed them, delete the old chunks from the vector DB by document ID, and insert the new chunks. Most vector databases support document-level deletion by metadata filter.
What is the biggest mistake teams make building their first RAG system?: Skipping retrieval evaluation. Teams build the pipeline, run a few manual tests, and ship. In production, retrieval fails on 20-40% of queries because the chunks are too large, the embedding model doesn't match the query vocabulary, or relevant context is split across chunk boundaries. Measure Retrieval Recall@5 before shipping.

Related Use Cases

Building AI Chatbots with LLM APIs

Building a reliable, production-grade AI chatbot is significantly more complex than callin...

AI Data Extraction from Unstructured Text

Valuable data is locked in unstructured formats: PDF invoices, email threads, contract PDF...

AI Document and Meeting Summarisation

Information overload is a universal productivity problem. Long meeting transcripts, resear...