Agentic RAG Best Practices: A Complete Guide for Building AI Apps With PostgreSQL

Developers using Timescale, pgvector, and pgai have been asking for clear guidance and best practices on building agentic RAG (retrieval-augmented generation) applications. 

You’re frustrated with "RAG in 30 seconds" videos that work as shiny demos but collapse instantly when applied to real production workloads. 

You've scrolled through endless X/Twitter threads but can't tell which advice is actually reliable for your specific business use case. 

You’re tired of finding out about robust architectural decisions only after you’ve spent two weeks committed to an unscalable approach. 

Is this you? 

The tough part about building agentic retrieval isn’t just implementing basic retrieval, it’s deeply understanding why certain approaches work and when to try something different. Important preparation steps like choosing the right documents and files for contextual retrieval happen way before even a single line of retrieval code is written, something “complete agentic RAG cheatsheet” LinkedIn posts (there’s no doubt you’ve seen one of these) don’t ever mention. 

This series will be the first to provide comprehensive guidance on building intelligent agents with pgai and pgvector, covering not just how to implement features, but why and when to use different approaches. 

At Timescale, we believe that dedicated vector databases are the wrong abstraction. Most devs already use PostgreSQL—why manage another piece of infrastructure when PostgreSQL is perfectly performant for AI agent workloads too? 

Agentic RAG Best Practices: What We're Building

👉🏻 Watch the one-minute video summary. 

We're creating a comprehensive guide that takes you from start to finish in building RAG applications with PostgreSQL. 

The series will cover: 

1. Document gathering, parsing, and loading
2. Document chunking strategies
3. Tool calling / function calling / MCP 
4. Embedding generation and storage
5. Vector indexing and retrieval
6. LLM prompting for accurate retrieval
7. Performance optimization (indexing, scaling, caching)
8. Monitoring and benchmarking
9. Security and access controls
10. Evaluating retrieval effectiveness (evals)

New Guide Every Two Weeks 

We're releasing a new guide every two weeks, starting today with our first article on document gathering, parsing, and loading. Each guide will provide practical, hands-on advice for implementing agentic RAG with PostgreSQL.

The complete series will also be available as an O'Reilly ebook once finished.

Get Involved 

Our first guide on document preparation is available now. Whether you're new to AI or an experienced developer looking to implement agentic RAG with PostgreSQL, this series will give you the foundation you need.

Stay tuned for our next guide on chunking strategies, coming in two weeks.

In the meantime, we'd love to see you share your thoughts, questions, and suggestions on social media and Discord:

• Join our Discord Community: Get real-time answers from the Timescale team and connect with other developers.
• Follow us on social media: Stay updated with the latest from Timescale on X/Twitter and LinkedIn.
• Connect with Jacky (developer advocate): Follow me for more practical AI and PostgreSQL content on X/Twitter, Threads, and TikTok.
• Direct questions: Have a specific question about your agentic retrieval implementation? Ask me anything at jacky (at) timescale (dot) com.

We're building this guide for you, so don't hesitate to let us know what topics you'd like us to cover in future installments!