RAG Best Practices — Production Pipeline Guide 2026

What it is

This guide covers best practices for building production Retrieval-Augmented Generation (RAG) pipelines. It addresses chunking strategies, embedding model selection, vector database setup, retrieval techniques, evaluation methods, and common pitfalls with code examples.

The guide targets ML engineers, backend developers, and AI product teams building search or question-answering systems that ground LLM responses in retrieved documents.

The project is actively maintained and suitable for both individual developers and teams looking to integrate it into their existing toolchain. Documentation and community support are available for onboarding.

How it saves time or tokens

Proper RAG architecture reduces token usage by retrieving only relevant document chunks instead of stuffing entire documents into context. Good chunking and retrieval strategies improve answer quality while keeping prompt sizes manageable. The estimated token budget for this workflow is around 3,200 tokens.

For teams evaluating multiple tools in the same category, the clear documentation and active community reduce the time spent on research and troubleshooting. Getting started takes minutes rather than hours of configuration.

How to use

1. Choose a chunking strategy based on your document type (fixed-size, semantic, recursive character splitting).
2. Select an embedding model (text-embedding-3-small for cost efficiency, text-embedding-3-large for quality).
3. Index chunks in a vector database (pgvector, Milvus, Pinecone, or Weaviate).
4. Implement retrieval with hybrid search (dense vectors + sparse BM25) for best recall.
5. Evaluate with metrics like recall@k, MRR, and end-to-end answer correctness.

Example

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_postgres import PGVector

# Chunk documents
splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,
    chunk_overlap=50,
    separators=['\n\n', '\n', '. ', ' ']
)
chunks = splitter.split_documents(documents)

# Index in pgvector
embeddings = OpenAIEmbeddings(model='text-embedding-3-small')
vectorstore = PGVector.from_documents(
    chunks, embeddings,
    connection='postgresql://user:pass@localhost/ragdb'
)

# Retrieve and generate
retriever = vectorstore.as_retriever(search_kwargs={'k': 5})
results = retriever.invoke('How do I configure the payment gateway?')

Related on TokRepo

• AI Tools for RAG — Browse RAG frameworks, vector databases, and embedding tools.
• AI Memory Providers — Explore memory systems that complement RAG pipelines.

Common pitfalls

• Using chunk sizes that are too large (>1000 tokens). Large chunks dilute relevance and waste context window. Start with 256-512 tokens and tune based on recall metrics.
• Skipping evaluation entirely. Without measuring retrieval recall and answer correctness, you cannot tell if changes improve or degrade quality.
• Relying on vector similarity alone. Hybrid search combining dense embeddings with sparse keyword matching (BM25) consistently outperforms either method alone.
• Applying the skill without reading the documentation first. Each skill has specific prerequisites and configuration requirements that affect the quality of results.